@article{doi:10.1002/for.3980130102,
author = {Lin, Jin-Lung and Granger, C. W. J.},
title = {Forecasting from non-linear models in practice},
journal = {Journal of Forecasting},

volume = {13},
number = {1},
pages = {1-9},
keywords = {Non-linear models, multi-step forecasts, bootstrap estimates},
doi = {10.1002/for.3980130102},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980130102},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.3980130102},
abstract = {Abstract If a simple non-linear autoregressive time-series model is suggested for a series, it is not straightforward to produce multi-step forecasts from it. Several alternative theoretical approaches are discussed and then compared with a simulation study only for the two-step case. It is suggested that fitting a new model for each forecast horizon may be a satisfactory strategy.},
year = {1994}
}

@Misc{Greenwich_2019,
author = "Johnson, Richard",
title = {Demystifying Alternative Data},
year = {2019},
Howpublished  = {https://www.greenwich.com/asset-management/demystifying-alternative-data},
publisher={Greenwich Associates},
Note = {Last accessed on July 6, 2019}
}


@book{Wickham:2017:RDS:3086927,
 author = {Wickham, Hadley and Grolemund, Garrett},
 title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
 year = {2017},
 isbn = {1491910399, 9781491910399},
 edition = {1st},
 publisher = {O'Reilly Media, Inc.},
}

@Book{Choudhry:2007,
  title={An Introduction to Value-at-Risk},
  author={Choudhry, M. and Tanna, K.},
  isbn={9780470033777},
  series={Securities Institute},
  year={2007},
  publisher={Wiley}
}

@book{McCulloh:2013:SNA:2829081,
 author = {McCulloh, I. and Armstrong, H. and Johnson, A.},
 title = {Social Network Analysis with Applications},
 year = {2013},
 isbn = {1118169476, 9781118169476},
 edition = {1st},
 publisher = {Wiley Publishing},
}


@book{CaseBerg:01,
  abstract = {{This book builds theoretical statistics from the first
		  principles of probability theory. Starting from the basics
		  of probability, the authors develop the theory of
		  statistical inference using techniques, definitions, and
		  concepts that are statistical and are natural extensions
		  and consequences of previous concepts. Intended for
		  first-year graduate students, this book can be used for
		  students majoring in statistics who have a solid
		  mathematics background. It can also be used in a way that
		  stresses the more practical uses of statistical theory,
		  being more concerned with understanding basic statistical
		  concepts and deriving reasonable statistical procedures for
		  a variety of situations, and less concerned with formal
		  optimality investigations.}},
  added-at = {2009-10-28T04:42:52.000+0100},
  author = {Casella, G. and Berger, R.},
  biburl = {https://www.bibsonomy.org/bibtex/21597678f36e23439610affbf46adec1c/jwbowers},
  citeulike-article-id = {105644},
  date-added = {2007-09-03 22:45:16 -0500},
  date-modified = {2007-09-03 22:45:16 -0500},
  howpublished = {{Textbook Binding}},
  interhash = {2dd8caad6c0b6fb80e6334986a231a05},
  intrahash = {1597678f36e23439610affbf46adec1c},
  isbn = {0534243126},
  keywords = {methodology probability statistics},
  month = {June},
  opturl = {http://www.amazon.fr/exec/obidos/ASIN/0534243126/citeulike04-21},
  publisher = {{Duxbury Resource Center}},
  timestamp = {2009-10-28T04:42:57.000+0100},
  title = {Statistical Inference},
  year = 2001
}



@article{Sun2016,
abstract = {•Transitions between patterns are emergent properties in spatial epidemics.•Two types of pattern transitions in infectious diseases are shown.•We provide possible mechanisms of pattern transition in spatial epidemics.•Pattern transition promotes complexity in spatial epidemics.•The results are applicable in medical science, ecology, quantitative finance and so on.},
affiliation = {Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, PR China; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Department of Vector Ecology and Environment, Nagasaki University Institute of Tropical Medicine (NEKKEN), Nagasaki 852-8523, Japan; Center of Mathematics for Social Creativity, Hokkaido University, Sapporo 060-0812, Japan; Department of Mathematics, Southeast University, Nanjing 210096, PR China; Department of Mathematics and Statistics, University of Victoria, Victoria BC V8W 3R4, Canada; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan},
author = {Sun, G. and Jusup, M. and Jin, Z. and Wang, Y. and Wang, Z.},
doi = {10.1016/j.plrev.2016.08.002},
journal = {Physics of Life Reviews},
keywords = {Reaction–diffusion equation; Cellular automata; Spatial heterogeneity; Seasonality and noise; Coherence resonance; Cyclic evolution},
language = {English},
number = {Complete},
pages = {43-73},
title = {Review},
volume = {19},
year = {2016},
}

@article{DBLP:journals/amc/Li15a,
  author    = {Li L.},
  title     = {Patch invasion in a spatial epidemic model},
  journal   = {Applied Mathematics and Computation},
  volume    = {258},
  pages     = {342--349},
  year      = {2015},
  url       = {http://dx.doi.org/10.1016/j.amc.2015.02.006},
  doi       = {10.1016/j.amc.2015.02.006},
  timestamp = {Sat, 21 Mar 2015 13:18:42 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/amc/Li15a},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}


@article{Sun20141507,
title = "Influence of time delay and nonlinear diffusion on herbivore outbreak ",
journal = "Communications in Nonlinear Science and Numerical Simulation ",
volume = "19",
number = "5",
pages = "1507 - 1518",
year = "2014",
note = "",
issn = "1007-5704",
doi = "http://doi.org/10.1016/j.cnsns.2013.09.016",
url = "http://www.sciencedirect.com/science/article/pii/S1007570413004164",
author = "G. Sun and A. Chakraborty and Q. Liu and Z. Jin and K. E. Anderson and B. Li",
keywords = "Herbivore-plant",
keywords = "Time delay",
keywords = "Spatial diffusion",
keywords = "Outbreak",
keywords = "Synchrony "
}


@article{PhysRevE.90.042807,
  title = {Nonlinear growth and condensation in multiplex networks},
  author = {Nicosia, V. and Bianconi, G. and Latora, V. and Barthelemy, M.},
  journal = {Phys. Rev. E},
  volume = {90},
  issue = {4},
  pages = {042807},
  numpages = {13},
  year = {2014},
  month = {Oct},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.90.042807},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.90.042807}
}

@Article{Lacasa2015,
author={Lacasa, L.
and Nicosia, V.
and Latora, V.},
title={Network structure of multivariate time series},
journal={Scientific Reports},
year={2015},
month={Oct},
day={21},
publisher={The Author(s) SN  -},
volume={5},
pages={15508 EP  -},
note={Article},
url={http://dx.doi.org/10.1038/srep15508}
}



@article{Lü20111150,
title = "Link prediction in complex networks: A survey ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "390",
number = "6",
pages = "1150 - 1170",
year = "2011",
note = "",
issn = "0378-4371",
doi = "http://doi.org/10.1016/j.physa.2010.11.027",
url = "http://www.sciencedirect.com/science/article/pii/S037843711000991X",
author = "L. Lü and T. Zhou",
keywords = "Link prediction",
keywords = "Complex networks",
keywords = "Node similarity",
keywords = "Maximum likelihood methods",
keywords = "Probabilistic models "
}




@article{1367-2630-17-7-073029,
  author={E. Cozzo and M. Kivelä and M. De Domenico and A. Solé-Ribalta and A. Arenas and S. Gómez and M. A.
Porter and Y. Moreno},
  title={Structure of triadic relations in multiplex networks},
  journal={New Journal of Physics},
  volume={17},
  number={7},
  pages={073029},
  url={http://stacks.iop.org/1367-2630/17/i=7/a=073029},
  year={2015},
  abstract={Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.}
}


@article{PhysRevLett.111.058702,
  title = {Coevolution and Correlated Multiplexity in Multiplex Networks},
  author = {Kim, J. Y. and Goh, K. I.},
  journal = {Phys. Rev. Lett.},
  volume = {111},
  issue = {5},
  pages = {058702},
  numpages = {5},
  year = {2013},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.111.058702},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.111.058702}
}


@Article{Battiston2017,
author="Battiston, F.
and Nicosia, V.
and Latora, V.",
title="The new challenges of multiplex networks: Measures and models",
journal="The European Physical Journal Special Topics",
year="2017",
volume="226",
number="3",
pages="401--416",
abstract="What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than only by the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.",
issn="1951-6401",
doi="10.1140/epjst/e2016-60274-8",
url="http://dx.doi.org/10.1140/epjst/e2016-60274-8"
}


@article{SandovalJunior2012187,
title = "Correlation of financial markets in times of crisis ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "391",
number = "1–2",
pages = "187 - 208",
year = "2012",
note = "",
issn = "0378-4371",
doi = "http://dx.doi.org/10.1016/j.physa.2011.07.023",
url = "http://www.sciencedirect.com/science/article/pii/S037843711100570X",
author = "L. S. Junior and I. De P. Franca",
keywords = "Financial markets",
keywords = "Crisis",
keywords = "Correlation matrix",
keywords = "Random matrix theory "
}

@article{10.1371/journal.pone.0107056,
    author = {Tan, F. AND Xia, Y. AND Zhu, B.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Link Prediction in Complex Networks: A Mutual Information Perspective},
    year = {2014},
    month = {09},
    volume = {9},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0107056},
    pages = {1-8},
    abstract = {Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.},
    number = {9},
    doi = {10.1371/journal.pone.0107056}
}

@article{PhysRevLett.86.3200,
  title = {Epidemic Spreading in Scale-Free Networks},
  author = {Pastor-Satorras, R. and Vespignani, A.},
  journal = {Phys. Rev. Lett.},
  volume = {86},
  issue = {14},
  pages = {3200--3203},
  numpages = {0},
  year = {2001},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.86.3200},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.86.3200}
}


@Inbook{Barabasi2003,
author="Barab{\'a}si, Albert-L{\'a}szl{\'o}
and Ravasz, Erzs{\'e}bet
and Oltvai, Zolt{\'a}n",
editor="Pastor-Satorras, R.
and Rubi, M.
and Diaz-Guilera, A.",
title="Hierarchical Organization of Modularity in Complex Networks",
bookTitle="Statistical Mechanics of Complex Networks",
year="2003",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="46--65",
isbn="978-3-540-44943-0",
doi="10.1007/978-3-540-44943-0_4",
url="http://dx.doi.org/10.1007/978-3-540-44943-0_4"
}


@article{0295-5075-97-6-68006,
  author={G. D'Agostino and A. Scala and V. Zlatić and G. Caldarelli},
  title={Robustness and assortativity for diffusion-like processes in scale-free networks},
  journal={EPL (Europhysics Letters)},
  volume={97},
  number={6},
  pages={68006},
  url={http://stacks.iop.org/0295-5075/97/i=6/a=68006},
  year={2012}
}

@article{10.1371/journal.pone.0031929,
    author = {Song, W.-M. AND Di Matteo, T. AND Aste, T.},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Hierarchical Information Clustering by Means of Topologically Embedded Graphs},
    year = {2012},
    month = {03},
    volume = {7},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031929},
    pages = {1-14},
    abstract = {<p>We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.</p>},
    number = {3},
    doi = {10.1371/journal.pone.0031929}
}

@article{doi:10.1137/070710111,
author = {A. Clauset and C. R. Shalizi and M. E. J. Newman},
title = {Power-Law Distributions in Empirical Data},
journal = {SIAM Review},
volume = {51},
number = {4},
pages = {661-703},
year = {2009},
doi = {10.1137/070710111},

URL = {
        http://dx.doi.org/10.1137/070710111

},
eprint = {
        http://dx.doi.org/10.1137/070710111

}

}

@article{PhysRevLett.104.108702,
  title = {Entropic Origin of Disassortativity in Complex Networks},
  author = {Johnson, S. and Torres, J. J. and Marro, J. and Mu\~noz, M. A.},
  journal = {Phys. Rev. Lett.},
  volume = {104},
  issue = {10},
  pages = {108702},
  numpages = {4},
  year = {2010},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.104.108702},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.104.108702}
}


@article{1742-5468-2012-07-P07025,
  author={G. Livan and J. Inoue and E. Scalas},
  title={On the non-stationarity of financial time series: impact on optimal portfolio selection},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  volume={2012},
  number={07},
  pages={P07025},
  url={http://stacks.iop.org/1742-5468/2012/i=07/a=P07025},
  year={2012},
  abstract={We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices.}
}


@article{PhysRevE.94.062306,
  title = {Parsimonious modeling with information filtering networks},
  author = {Barfuss, W. and Massara, G. P. and Di Matteo, T. and Aste, T.},
  journal = {Phys. Rev. E},
  volume = {94},
  issue = {6},
  pages = {062306},
  numpages = {12},
  year = {2016},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.94.062306},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.94.062306}
}


@ARTICLE{2016arXiv160207349B,
   author = {{Barfuss}, W. and {Massara}, G. P. and {Di Matteo}, T. and
{Aste}, T.},
    title = "{Parsimonious modeling with Information Filtering Networks}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1602.07349},
 primaryClass = "cs.IT",
 keywords = {Computer Science - Information Theory, Statistics - Machine Learning},
     year = 2016,
    month = feb
}


@article{Morales20136470,
title = "Non-stationary multifractality in stock returns ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "392",
number = "24",
pages = "6470 - 6483",
year = "2013",
note = "",
issn = "0378-4371",
doi = "http://dx.doi.org/10.1016/j.physa.2013.08.037",
url = "http://www.sciencedirect.com/science/article/pii/S0378437113007668",
author = "R. Morales and T. Di Matteo and T. Aste",
keywords = "Multifractality",
keywords = "Generalised Hurst exponent",
keywords = "Multifractal models "
}


@article{song2008analysis,
  title={Analysis on filtered correlation graph for information extraction},
  author={Song, W.-M. and Aste, T. and Di Matteo, T.},
  journal={Statistical Mechanics of Molecular Biophysics},
  pages={88},
  year={2008}
}

@article{musmeci2016interplay,
  title={Interplay between past market correlation structure changes and future volatility outbursts},
  author={Musmeci, N. and Aste, T. and Di Matteo, T.},
  journal={Scientific Reports},
  volume={6},
  pages={36320},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{pozzi2008centrality,
  title={Centrality and peripherality in filtered graphs from dynamical financial correlations},
  author={Pozzi, F. and Di Matteo, T. and Aste, T.},
  journal={Advances in Complex Systems},
  volume={11},
  number={06},
  pages={927--950},
  year={2008},
  publisher={World Scientific Publishing Company}
}

@book{aitchison1957lognormal,
  title={The Lognormal Distribution: With Special Reference to Its Uses in Economics},
  author={Aitchison, J. and Brown, J.A.C.},
  lccn={a58001106},
  series={Cambridge. University. Dept. of Applied Economics. Monographs, 5},
  year={1957},
  publisher={University Press}
}

@book{granger1992using,
  title={Using the Mutual Information Coefficient to Identify Lags in Non-linear Models},
  author={Granger, C.W.J. and Lin, J.L.},
  series={Discussion paper: Jingji-Yanjiusuo},
  year={1992},
  publisher={Institute of Economics, Academia Sinica}
}

@inproceedings{pozzi2008dynamical,
  title={Dynamical correlations in financial systems [6802-54]},
  author={Pozzi, F. and Aste, T. and Rotundo, G. and Di Matteo, T.},
  booktitle={PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING},
  volume={6802},
  pages={6802},
  year={2008},
  organization={International Society for Optical Engineering; 1999}
}

@article{918bf1f7d91649f59a1173af04bfc06a,
title = "Risk diversification: a study of persistence with a filtered correlation-network approach",
author = "N. Musmeci and T. {Di Matteo} and T. Aste",
year = "2015",
month = "3",
volume = "1",
pages = "1--22",
journal = "Journal of Network Theory in Finance",
number = "1",

}

@Article{Zheng2012,
author={Zheng, Z.
and Podobnik, B.
and Feng, L.
and Li, Baowen},
title={Changes in Cross-Correlations as an Indicator for Systemic Risk},
year={2012},
month={Nov},
day={26},
volume={2},
issn={2045-2322},
journal={Scientific reports},
  publisher={Nature Publishing Group},
pages = {Article number 888}
}



@article{PhysRevE.88.012806,
  title = {Evolution of correlation structure of industrial indices of U.S. equity markets},
  author = {Buccheri, G. and Marmi, S. and Mantegna, R. N.},
  journal = {Phys. Rev. E},
  volume = {88},
  issue = {1},
  pages = {012806},
  numpages = {7},
  year = {2013},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.88.012806},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.88.012806}
}



@inproceedings{pozzi2009use,
  title={The use of topological quantities to detect hierarchical properties in financial markets: the Financial sector in NYSE},
  author={Pozzi, F. and Aste, T. and Shaw, W. and Di Matteo, T. and Mastorakis, N. E. and Croitoru, A. and Balas, V. E. and Son, E. and Mladenov, V.},
  booktitle={WSEAS International Conference. Proceedings. Recent Advances in Computer Engineering},
  number={10},
  year={2009},
  organization={WSEAS}
}


@article{song2012hierarchical,
  title={Hierarchical information clustering by means of topologically embedded graphs},
  author={Song, W.-M. and Di Matteo, T. and Aste, T.},
  journal={PLoS One},
  volume={7},
  number={3},
  pages={e31929},
  year={2012},
  publisher={Public Library of Science}
}



@article{Battiston06092016,
author = {Battiston, S. and Caldarelli, G. and May, R. M. and Roukny, T. and Stiglitz, J. E.},
title = {The price of complexity in financial networks},
volume = {113},
number = {36},
pages = {10031-10036},
year = {2016},
doi = {10.1073/pnas.1521573113},
URL = {http://www.pnas.org/content/113/36/10031.abstract},
eprint = {http://www.pnas.org/content/113/36/10031.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}



@Manual{R2015,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2014},
    url = {http://www.R-project.org/},
  }


@article{citeulike:1447442,
    author = {Hlavackovaschindler, K. and Palus, M. and Vejmelka, M. and Bhattacharya, J.},
    doi = {10.1016/j.physrep.2006.12.004},
    issn = {03701573},
    journal = {Physics Reports},
    month = mar,
    number = {1},
    pages = {1--46},
    title = {Causality detection based on information-theoretic approaches in time series analysis},
    url = {http://dx.doi.org/10.1016/j.physrep.2006.12.004},
    volume = {441},
    year = {2007}
}

@article{Baghli2006380,
title = "A model-free characterization of causality ",
journal = "Economics Letters ",
volume = "91",
number = "3",
pages = "380 - 388",
year = "2006",
note = "",
issn = "0165-1765",
doi = "http://dx.doi.org/10.1016/j.econlet.2005.12.016",
url = "http://www.sciencedirect.com/science/article/pii/S0165176505004234",
author = "Mustapha Baghli",
keywords = "Causality in information",
keywords = "Information-theoretic statistics",
keywords = "Nonlinearity",
keywords = "Fast double bootstrap test "
}


@article{Barberis1998307,
title = "A model of investor sentiment ",
journal = "Journal of Financial Economics ",
volume = "49",
number = "3",
pages = "307 - 343",
year = "1998",
note = "",
issn = "0304-405X",
doi = "http://dx.doi.org/10.1016/S0304-405X(98)00027-0",
url = "http://www.sciencedirect.com/science/article/pii/S0304405X98000270",
author = "Nicholas Barberis and Andrei Shleifer and Robert Vishny",
keywords = "Investor sentiment",
keywords = "Underreaction",
keywords = "Overreaction "
}


@article{lizier2010differentiating,
  title={Differentiating information transfer and causal effect},
  author={Lizier, Joseph T. and Prokopenko, Mikhail},
  journal={The European Physical Journal B},
  volume={73},
  number={4},
  pages={605--615},
  year={2010},
  publisher={Springer}
}

@article{PhysRevLett.85.461,
  title = {Measuring Information Transfer},
  author = {Schreiber, Thomas},
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  issue = {2},
  pages = {461--464},
  numpages = {0},
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  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.85.461},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.85.461}
}


@article{Huang01032015,
author = {Huang, Dashan and Jiang, Fuwei and Tu, Jun and Zhou, Guofu},
title = {Investor Sentiment Aligned: A Powerful Predictor of Stock Returns},
volume = {28},
number = {3},
pages = {791-837},
year = {2015},
doi = {10.1093/rfs/hhu080},
journal = {Review of Financial Studies}
}


@inproceedings{Sehgal:2007:SSP:1335998.1336036,
 author = {Sehgal, Vivek and Song, Charles},
 title = {SOPS: Stock Prediction Using Web Sentiment},
 booktitle = {Proceedings of the Seventh IEEE International Conference on Data Mining Workshops},
 series = {ICDMW '07},
 year = {2007},
 isbn = {0-7695-3033-8},
 pages = {21--26},
 numpages = {6},
 url = {http://dx.doi.org/10.1109/ICDMW.2007.97},
 doi = {10.1109/ICDMW.2007.97},
 acmid = {1336036},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
}

@INPROCEEDINGS{Zhang_tradingstrategies,
    author = {Wenbin Zhang and Steven Skiena},
    title = {Trading strategies to exploit blog and news sentiment},
    booktitle = {In Fourth Int. Conf. on Weblogs and Social Media (ICWSM), 2010. – 186},
    year = {2010}
}


@article{wrap54525,
          volume = {Volume 3},
           month = {May},
          author = {H. S. Moat and C. Curme and A. Avakian and D. Y. Kenett and H. E. Stanley and T. Preis},
           title = {Quantifying wikipedia usage patterns before stock market moves},
       publisher = {Nature Publishing Group},
         journal = {Scientific Reports},
           pages = {Article number 1801},
            year = {2013}
}



@article {JOFI:JOFI1493,
author = {FANG, LILY and PERESS, JOEL},
title = {Media Coverage and the Cross-section of Stock Returns},
journal = {The Journal of Finance},
volume = {64},
number = {5},
publisher = {Blackwell Publishing Inc},
issn = {1540-6261},
doi = {10.1111/j.1540-6261.2009.01493.x},
pages = {2023--2052},
year = {2009},
}


@article{doi:10.1080/14697688.2015.1039865,
author = {Germ\'{a}n G. Creamer},
title = {Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model?},
journal = {Quantitative Finance},
volume = {15},
number = {8},
pages = {1405-1416},
year = {2015},
doi = {10.1080/14697688.2015.1039865},

URL = {
        http://dx.doi.org/10.1080/14697688.2015.1039865

},
eprint = {
        http://dx.doi.org/10.1080/14697688.2015.1039865

}

}



@article{Doukas:2009,
author = {John A. Doukas and Meng Li},
title = {Asymmetric Asset Price Reaction to News and Arbitrage Risk},
journal = {Review of Behavioural Finance},
volume = {1},
number = {1/2},
pages = {23-43},
year = {2009},
doi = {10.1108/19405979200900002}
}

@article{Antweiler+Frank:04a,
  author = {Antweiler, Werner and Frank, Murray Z.},
  journal = {Journal of Finance},
  number = 3,
  pages = {1259--1294},
  title = {Is All That Talk Just Noise? {The} Information Content of Internet
Stock Message Boards},
  volume = 59,
  year = 2004
}



@article{mao2014quantifying,
  title={Quantifying the effects of online bullishness on international financial markets},
  author={Mao, H. and Counts, S. and Bollen, J.},
  journal={European Central Bank Workshop on Using Big Data for Forecasting and Statistics, Frankfurt, Germany},
  year={2014}
}

@incollection{Darbellay1998,
year={1998},
isbn={978-1-4612-7273-1},
booktitle={Signal Analysis and Prediction},
series={Applied and Numerical Harmonic Analysis},
editor={Prochazka, Ales and Uhlir, Jan and Rayner, P.W.J. and Kingsbury, N.G.},
doi={10.1007/978-1-4612-1768-8_18},
title={Predictability: An Information-Theoretic Perspective},
publisher={Birkhauser Boston},
author={Darbellay, GeorgesA.},
pages={249-262},
language={English}
}


@article{doi:10.1080/15427560.2017.1332061,
author = {Matthias W. Uhl},
title = {Emotions Matter: Sentiment and Momentum in Foreign Exchange},
journal = {Journal of Behavioral Finance},
volume = {18},
number = {3},
pages = {249-257},
year  = {2017},
publisher = {Routledge},
doi = {10.1080/15427560.2017.1332061},

URL = {
        https://doi.org/10.1080/15427560.2017.1332061

},
eprint = {
        https://doi.org/10.1080/15427560.2017.1332061

}

}



@article{doi:10.1080/14697688.2016.1211797,
author = {Daniel Tsvetanov and Jerry Coakley and Neil Kellard},
title = {Is news related to GDP growth a risk factor for commodity futures returns?},
journal = {Quantitative Finance},
volume = {16},
number = {12},
pages = {1887-1899},
year  = {2016},
publisher = {Routledge},
doi = {10.1080/14697688.2016.1211797},

URL = {
        https://doi.org/10.1080/14697688.2016.1211797

},
eprint = {
        https://doi.org/10.1080/14697688.2016.1211797

}

}


@ARTICLE{RePEc:eee:empfin:v:18:y:2011:i:2:p:321-340,
title = {When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions},
author = {Groß-Klußmann, Axel and Hautsch, Nikolaus},
year = {2011},
journal = {Journal of Empirical Finance},
volume = {18},
number = {2},
pages = {321-340},
abstract = {We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. We show that a classification of news according to indicated relevance is crucial to filter out noise and to identify significant effects. Moreover, sentiment indicators have predictability for future price trends though the profitability of news-implied trading is deteriorated by increased bid-ask spreads.},
keywords = {Firm-specific news News sentiment High-frequency data Volatility Liquidity Abnormal returns},
url = {https://EconPapers.repec.org/RePEc:eee:empfin:v:18:y:2011:i:2:p:321-340}
}

@article{doi:10.1111/j.1540-6261.2009.01493.x,
author = {FANG, LILY and PERESS, JOEL},
title = {Media Coverage and the Cross-section of Stock Returns},
journal = {The Journal of Finance},
volume = {64},
number = {5},
pages = {2023-2052},
doi = {10.1111/j.1540-6261.2009.01493.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.2009.01493.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.2009.01493.x},
abstract = {ABSTRACT By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns.}
}

@article{Vicente:2011,
year={2011},
issn={0929-5313},
journal={Journal of Computational Neuroscience},
volume={30},
number={1},
doi={10.1007/s10827-010-0262-3},
title={Transfer entropy—a model-free measure of effective connectivity for the neurosciences},
url={http://dx.doi.org/10.1007/s10827-010-0262-3},
publisher={Springer US},
author={Vicente, Raul and Wibral, Michael and Lindner, Michael and Pipa, Gordon},
pages={45-67},
language={English}
}

@article{PhysRevLett.103.238701,
  title = {Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables},
  author = {Barnett, Lionel and Barrett, Adam B. and Seth, Anil K.},
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  volume = {103},
  issue = {23},
  pages = {238701},
  numpages = {4},
  year = {2009},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.103.238701},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.103.238701}
}


@incollection{Dzeroski:2014,
year={2014},
isbn={978-3-319-11811-6},
booktitle={Discovery Science},
volume={8777},
series={Lecture Notes in Computer Science},
editor={D\u{z}eroski, Sa\u{s}o and Panov, Pan\u{c}e and Kocev, Dragi and Todorovski, Ljup\u{c}o},
doi={10.1007/978-3-319-11812-3_2},
title={Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market},
url={http://dx.doi.org/10.1007/978-3-319-11812-3_2},
publisher={Springer International Publishing},
keywords={Wrapper feature selection; Bayesian Networks; Stock microblogging sentiment},
author={Al Nasseri, Alya and Tucker, Allan and de Cesare, Sergio},
pages={13-24}
}

@Article {RePEc:dur:durham:2011_06,
title = {Media Sentiment and UK Stock Returns},
journal = {Working Papers},
year = {2011},
author = {Nicky J. Ferguson and Jie Michael Guo and Nicky Herbert Y.T. Lam and Dennis Philip},
publisher = {Durham University Business School}
}

@article{10.1371/journal.pone.0138441,
    author = {Ranco, G. AND Aleksovski, D. AND Caldarelli, G. AND Gr\u{c}ar, M. AND Mozeti\u{c}, I.},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {The Effects of \uppercase{t}witter Sentiment on Stock Price Returns},
    year = {2015},
    month = {09},
    volume = {10},
    url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0138441},
    pages = {e0138441},
    number = {9},
    doi = {10.1371/journal.pone.0138441}
}

@Article{ICWSM101513,
author = {Eric Gilbert and Karrie Karahalios},
title = {Widespread Worry and the Stock Market},
conference = {International AAAI Conference on Web and Social Media},
year = {2010},
pages = {58--65},
url = {https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1513/1833}
}

@incollection{Olaniyan:2015,
year={2015},
isbn={978-3-319-17090-9},
booktitle={Statistical Learning and Data Sciences},
volume={9047},
series={Lecture Notes in Computer Science},
editor={Gammerman, Alexander and Vovk, Vladimir and Papadopoulos, Harris},
doi={10.1007/978-3-319-17091-6_15},
title={Social Web-Based Anxiety Index's Predictive Information on S\&P 500 Revisited},
url={http://dx.doi.org/10.1007/978-3-319-17091-6\_15},
publisher={Springer International Publishing},
author={Olaniyan, Rapheal and Stamate, Daniel and Logofatu, Doina},
pages={203-213},
language={English}
}


@article{DBLP:journals/eswa/NasseriTC15,
  author    = {Alya Al Nasseri and
               Allan Tucker and
               Sergio de Cesare},
  title     = {Quantifying StockTwits semantic terms' trading behavior in financial
               markets: An effective application of decision tree algorithms},
  journal   = {Expert Syst. Appl.},
  volume    = {42},
  number    = {23},
  pages     = {9192--9210},
  year      = {2015},
  url       = {http://dx.doi.org/10.1016/j.eswa.2015.08.008},
  doi       = {10.1016/j.eswa.2015.08.008}
}


@Article {HestonRanjan:2014,
   author = {Heston, Steven L. and Sinha, Nitish Ranjan},
    title = {News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns},
  journal = {Robert H. Smith School Research Paper},
  URL = {http://ssrn.com/abstract=2311310},
    year = {2014}
}


@article{ENGELBERG2012260,
title = "How are shorts informed?: Short sellers, news, and information processing",
journal = "Journal of Financial Economics",
volume = "105",
number = "2",
pages = "260 - 278",
year = "2012",
issn = "0304-405X",
doi = "https://doi.org/10.1016/j.jfineco.2012.03.001",
url = "http://www.sciencedirect.com/science/article/pii/S0304405X12000384",
author = "Joseph E. Engelberg and Adam V. Reed and Matthew C. Ringgenberg",
keywords = "Asymmetric information, Manipulation, News media, Short sales"
}


@article{citeulike:12299800,
    author = {Preis, T. and Moat, H. S. and Stanley, H. E.},
    day = {25},
    doi = {10.1038/srep01684},
    issn = {2045-2322},
    journal = {Scientific Reports},
    month = apr,
    title = {{Quantifying Trading Behavior in Financial Markets Using Google Trends}},
    url = {http://dx.doi.org/10.1038/srep01684},
    volume = {3},
    year = {2013}
}

@article{Tumminello201040,
title = "Correlation, hierarchies, and networks in financial markets ",
journal = "Journal of Economic Behavior \& Organization ",
volume = "75",
number = "1",
pages = "40 - 58",
year = "2010",
note = "Transdisciplinary Perspectives on Economic Complexity ",
issn = "0167-2681",
doi = "http://dx.doi.org/10.1016/j.jebo.2010.01.004",
url = "http://www.sciencedirect.com/science/article/pii/S0167268110000077",
author = "Michele Tumminello and Fabrizio Lillo and Rosario N. Mantegna",
keywords = "Multivariate analysis",
keywords = "Hierarchical clustering",
keywords = "Correlation based networks",
keywords = "Bootstrap validation",
keywords = "Factor models",
keywords = "Kullback–Leibler distance "
}

@article{curme2015coupled,
  title={Coupled Network Approach To Predictability Of Financial Market Returns And News Sentiments},
  author={Curme, Chester and Stanley, H Eugene and Vodenska, Irena},
  journal={International Journal of Theoretical and Applied Finance},
  volume={18},
  number={07},
  pages={1550043},
  year={2015},
  publisher={World Scientific Publishing Company}
}

@inbook{Cont2005,
author="Cont, Rama",
editor="L{\'e}vy-V{\'e}hel, Jacques
and Lutton, Evelyne",
title="Long range dependence in financial markets",
bookTitle="Fractals in Engineering: New Trends in Theory and Applications",
year="2005",
publisher="Springer London",
address="London",
pages="159--179",
isbn="978-1-84628-048-1",
doi="10.1007/1-84628-048-6_11",
url="http://dx.doi.org/10.1007/1-84628-048-6_11"
}


@article{e15051643,
AUTHOR = {Wang, Gang-Jin and Xie, Chi and Chen, Yi-Jun and Chen, Shou},
TITLE = {Statistical Properties of the Foreign Exchange Network at Different Time Scales: Evidence from Detrended Cross-Correlation Coefficient and Minimum Spanning Tree},
JOURNAL = {Entropy},
VOLUME = {15},
YEAR = {2013},
NUMBER = {5},
PAGES = {1643},
URL = {http://www.mdpi.com/1099-4300/15/5/1643},
ISSN = {1099-4300},
DOI = {10.3390/e15051643}
}


@Article{Zhu2015,
author={Zhu, Boyao
and Xia, Yongxiang},
title={An information-theoretic model for link prediction in complex networks},
journal={Scientific Reports},
year={2015},
month={Sep},
day={03},
volume={5},
url={http://dx.doi.org/10.1038/srep13707}
}

@ARTICLE{Wolpert92stackedgeneralization,
    author = {David H. Wolpert},
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}

@book{surowiecki2004wisdom,
  title={The Wisdom of Crowds: Why the Many are Smarter Than the Few and how Collective Wisdom Shapes Business, Economies, Societies, and Nations},
  author={Surowiecki, J.},
  isbn={9780385503860},
  lccn={2004049822},
  url={https://books.google.com/books?id=bA0c4aYTD6gC},
  year={2004},
  publisher={Doubleday}
}

@article{onnela2003dynamic,
  title={Dynamic asset trees and Black Monday},
  author={Onnela, J-P and Chakraborti, Anirban and Kaski, Kimmo and Kertesz, Janos},
  journal={Physica A: Statistical Mechanics and its Applications},
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  number={1},
  pages={247--252},
  year={2003},
  publisher={Elsevier}
}

@article{onnela2003,
    author = {Onnela, J. P. and Chakraborti, A. and Kaski, K. and Kert{\'e}sz, J. and Kanto, A.},
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    journal = {Physica Scripta},
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    posted-at = {2011-04-10 15:17:18},
    priority = {2},
    title = {{Asset Trees and Asset Graphs in Financial Markets}},
    volume = {T106},
    year = {2003}
}

@book{faraway2006elm,
  added-at = {2009-10-28T04:42:52.000+0100},
  author = {Faraway, J. J.},
  biburl = {https://www.bibsonomy.org/bibtex/251af5204c48f76a2e74c2d994d98d6b3/jwbowers},
  date-added = {2007-09-03 22:45:16 -0500},
  date-modified = {2007-09-03 22:45:16 -0500},
  interhash = {93ca5c8a16d276853fbe632ec49cbc04},
  intrahash = {51af5204c48f76a2e74c2d994d98d6b3},
  keywords = {imported},
  publisher = {CRC Press},
  timestamp = {2009-10-28T04:43:07.000+0100},
  title = {{Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models}},
  year = 2006
}



@Article{refId0-Onnela-2004,
author={Onnela, J. P. and Kaski, K. and Kert{\'e}sz, J.},
title="Clustering and information in correlation based financial networks",
journal="The European Physical Journal B",
year="2004",
month="Mar",
day="01",
volume="38",
number="2",
pages="353--362",
abstract="Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10{\%} of the edges.",
issn="1434-6036",
doi="10.1140/epjb/e2004-00128-7",
url="https://doi.org/10.1140/epjb/e2004-00128-7"
}


@article{onnela2003bdynamics,
  title={Dynamics of market correlations: Taxonomy and portfolio analysis},
  author={Onnela, J. P. and Chakraborti, Anirban and Kaski, Kimmo and Kertesz, Janos and Kanto, Antti},
  journal={Physical Review E},
  volume={68},
  number={5},
  pages={056110},
  year={2003},
  publisher={APS}
}


@ARTICLE{RePEc:fau:aucocz:au2010_330,
title = {Dynamics of Stock Market Correlations},
author = {Kenett, Dror Y. and Shapira, Yoash and Madi, Asaf and Bransburg-Zabary, Sharron and Gur-Gershgoren, Gitit and Ben-Jacob, Eshel},
year = {2010},
journal = {Czech Economic Review},
volume = {4},
number = {3},
pages = {330-340},
abstract = {We present a novel approach to the study the dynamics of stock market correlations. This is achieved through an innovative visualization tool that allows an investigation of the structure and dynamics of the market, through the study of correlations. This is based on the Stock Market Holography (SMH) method recently introduced. This qualitative measure is complemented by the use of the eigenvalue entropy measure, to quantify how the information in the market changes in time. Using this innovative approach, we analyzed data from the New York Stock Exchange (NYSE), and the Tel Aviv Stock Exchange (TASE), for daily trading data for the time period of 2000–2009. This paper covers these new concepts for the study of financial markets in terms of structure and information as reflected by the changes in correlations over time.},
keywords = {Correlation; Stock Market Holography; eigenvalue entropy; sliding window},
url = {http://EconPapers.repec.org/RePEc:fau:aucocz:au2010_330}
}


@Article {PreisCurme:2014,
   author = {Curme, Chester and Preis, Tobias and Stanley, H. Eugene and Moat, Helen Susannah},
    title = {Quantifying the Semantics of Search Behavior Before Stock Market Moves},
  journal = {Proceedings of the National Academy of Sciences},
    year = {2014}
}

@article{citeulike:9128699,
    author = {Onnela, J. P. and Chakraborti, A. and Kaski, K. and Kert{\'{e}}sz, J. and Kanto, A.},
    citeulike-article-id = {9128699},
    journal = {Phys. Rev. E},
    number = {5},
    pages = {56110},
    posted-at = {2011-04-10 15:17:18},
    priority = {2},
    publisher = {APS},
    title = {{Dynamics of market correlations: Taxonomy and portfolio analysis}},
    volume = {68},
    year = {2003}
}

@article{10.1371/journal.pone.0017994,
    author = {Tumminello, Michele AND Miccichè, Salvatore AND Lillo, Fabrizio AND Piilo, Jyrki AND Mantegna, Rosario N.},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Statistically Validated Networks in Bipartite Complex Systems},
    year = {2011},
    month = {03},
    volume = {6},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0017994},
    pages = {1-11},
    number = {3},
    doi = {10.1371/journal.pone.0017994}
}

@Article{Mantegna1999,
author="Mantegna, R. N.",
title="Hierarchical structure in financial markets",
journal="The European Physical Journal B - Condensed Matter and Complex Systems",
year="1999",
volume="11",
number="1",
pages="193--197",
abstract="I find a hierarchical arrangement of stocks traded in a financial market by investigating the daily time series of the logarithm of stock price. The topological space is a subdominant ultrametric space associated with a graph connecting the stocks of the portfolio analyzed. The graph is obtained starting from the matrix of correlation coefficient computed between all pairs of stocks of the portfolio by considering the synchronous time evolution of the difference of the logarithm of daily stock price. The hierarchical tree of the subdominant ultrametric space associated with the graph provides a meaningful economic taxonomy.",
issn="1434-6036",
doi="10.1007/s100510050929",
url="http://dx.doi.org/10.1007/s100510050929"
}

@article{demiguel2009optimal,
  title={Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?},
  author={DeMiguel, Victor and Garlappi, Lorenzo and Uppal, Raman},
  journal={The Review of Financial Studies},
  volume={22},
  number={5},
  pages={1915--1953},
  year={2009},
  publisher={Oxford University Press}
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title = {Applications of news analytics in finance: A review},
author = {Mitra, L. and Mitra, G.},
publisher = {John Wiley \& Sons, Ltd.},
isbn = {9781118467411},
pages = {1--39},
booktitle = {The Handbook of News Analytics in Finance},
year = {2011},
}





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language={English}
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}


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@inbook {Mitra1:2011,
title = {Applications of news analytics in finance: A review},
author = {Mitra, L. and Mitra, G.},
publisher = {John Wiley \& Sons, Ltd.},
isbn = {9781118467411},
pages = {1--39},
booktitle = {The Handbook of News Analytics in Finance},
year = {2011},
}






@Article{Timm:2014,
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  title={{News or Noise? Using \uppercase{t}witter to Identify and Understand Company-specific News Flow}},
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  number={7-8},
  pages={791-830},
  month={09},
}

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@article{1506.02431,
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    title = {The Effects of \uppercase{T}witter Sentiment on Stock Price Returns},
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    url = {https://doi.org/10.1371/journal.pone.0138441},
    pages = {1-21},
    abstract = {Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.},
    number = {9},
    doi = {10.1371/journal.pone.0138441}
}


@incollection{Oliveira:2013,
year={2013},
isbn={978-3-642-40668-3},
booktitle={Progress in Artificial Intelligence},
volume={8154},
series={Lecture Notes in Computer Science},
doi={10.1007/978-3-642-40669-0_31},
title={On the Predictability of Stock Market Behavior Using StockTwits Sentiment and Posting Volume},
url={http://dx.doi.org/10.1007/978-3-642-40669-0_31},
publisher={Springer Berlin Heidelberg},
keywords={Microblogging Data; Returns; Trading Volume; Volatility; Regression},
author={Oliveira, Nuno and Cortez, Paulo and Areal, Nelson},
pages={355-365},
language={English}
}

@INPROCEEDINGS{Zhang_tradingstrategies,
    author = {Wenbin Zhang and Steven Skiena},
    title = {Trading strategies to exploit blog and news sentiment},
    booktitle = {In Fourth Int. Conf. on Weblogs and Social Media (ICWSM), 2010. – 186},
    year = {2010}
}

@ARTICLE{2014arXiv1405.3117S,
   author = {{Shi}, B. and {Ifrim}, G. and {Hurley}, N.},
    title = "{Be In The Know: Connecting News Articles to Relevant \uppercase{t}witter Conversations}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1405.3117},
 keywords = {Computer Science - Social and Information Networks, Computer Science - Information Retrieval, Physics - Physics and Society},
     year = 2014,
    month = may,
   adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1405.3117S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@INPROCEEDINGS{6924062,
author={Crone, S.F. and Koeppel, C.},
booktitle={Computational Intelligence for Financial Engineering Economics (CIFEr), 2104 IEEE Conference on},
title={Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons},
year={2014},
month={March},
pages={114-121},
doi={10.1109/CIFEr.2014.6924062}
}

@incollection{Smailovic:2013,
year={2013},
isbn={978-3-642-39145-3},
booktitle={Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data},
volume={7947},
series={Lecture Notes in Computer Science},
editor={Holzinger, Andreas and Pasi, Gabriella},
doi={10.1007/978-3-642-39146-0_8},
title={Predictive Sentiment Analysis of Tweets: A Stock Market Application},
url={http://dx.doi.org/10.1007/978-3-642-39146-0_8},
publisher={Springer Berlin Heidelberg},
keywords={stock market; Twitter; predictive sentiment analysis; sentiment classification; positive sentiment probability; Granger causality},
author={Smailović, Jasmina and Grčar, Miha and Lavrač, Nada and Žnidaršič, Martin},
pages={77-88},
language={English}
}


@inproceedings{Rao:2012:ASM:2456719.2456923,
 author = {Rao, Tushar and Srivastava, Saket},
 title = {Analyzing Stock Market Movements Using \uppercase{t}witter Sentiment Analysis},
 booktitle = {Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)},
 series = {ASONAM '12},
 year = {2012},
 isbn = {978-0-7695-4799-2},
 pages = {119--123},
 numpages = {5},
 url = {http://dx.doi.org/10.1109/ASONAM.2012.30},
 doi = {10.1109/ASONAM.2012.30},
 acmid = {2456923},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {Stock market, sentiment analysis, Twitter, microblogging, social network analysis},
}

@inproceedings{Sehgal:2007:SSP:1335998.1336036,
 author = {Sehgal, Vivek and Song, Charles},
 title = {SOPS: Stock Prediction Using Web Sentiment},
 booktitle = {Proceedings of the Seventh IEEE International Conference on Data Mining Workshops},
 series = {ICDMW '07},
 year = {2007},
 isbn = {0-7695-3033-8},
 pages = {21--26},
 numpages = {6},
 url = {http://dx.doi.org/10.1109/ICDMW.2007.97},
 doi = {10.1109/ICDMW.2007.97},
 acmid = {1336036},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
}

@INPROCEEDINGS{7821630,
author={O. Kolchyna and T. T. P. Souza and P. C. Treleaven and T. Aste},
booktitle={2016 Future Technologies Conference (FTC)},
title={A framework for \uppercase{t}witter events detection, differentiation and its application for retail brands},
year={2016},
volume={},
number={},
pages={323-331},
keywords={pattern clustering;retailing;sales management;social networking (online);text analysis;time series;Twitter event detection;Twitter event differentiation;retail brand;Twitter event quantification;sale spike prediction;spatial information;temporal information;topic information;Twitter event clustering;account growth signature;account relaxation signature;optimum event window;window selection;sales data;time series analysis;Twitter;Time series analysis;Event detection;Electronic mail;Data mining;Shape;Anomaly detection;clustering;event detection;event study;spikes;social media;Twitter},
doi={10.1109/FTC.2016.7821630},
ISSN={},
month={Dec},}



@article{vol2,
  title={Valuing illiquid common stock},
  author={Dyl, Edward A. and Jiang, George J.},
  journal={Financial Analysts Journal},
  volume={64},
  number={4},
  pages={40--47},
  year={2008},
  publisher={CFA Institute}
}

@article{1507.00955,
title = {\uppercase{t}witter sentiment analysis: Lexicon method, machine learning method
  and their combination.},
journal = {Handbook of Sentiment Analysis in Finance. ISBN 1910571571},
Author = {O. Kolchyna and T. T. P. Souza and P. Treleaven and T. Aste},
Year = {2016},
Eprint = {arXiv:1507.00955},
url = {http://arxiv.org/abs/1507.00955}
}

@INPROCEEDINGS{8252134,
author={J. Manfield and D. Lukacsko and T. T. P. Souza},
booktitle={2017 Computing Conference},
title={Bull bear balance: A cluster analysis of socially informed financial volatility},
year={2017},
volume={},
number={},
pages={421-428},
doi={10.1109/SAI.2017.8252134},
ISSN={},
month={July}}


@article{1402-4896-2003-T106-011,
  author={J. P. Onnela and A. Chakraborti and K. Kaski and J. Kertész and A. Kanto},
  title={Asset Trees and Asset Graphs in Financial Markets},
  journal={Physica Scripta},
  volume={2003},
  number={T106},
  pages={48},
  url={http://stacks.iop.org/1402-4896/2003/i=T106/a=011},
  year={2003},
  abstract={This paper introduces a new methodology for constructing a network of companies called a dynamic asset graph. This is similar to the dynamic asset tree studied recently, as both are based on correlations between asset returns. However, the new modified methodology does not, in general, lead to a tree but a disconnected graph. The asset tree, due to the minimum spanning tree criterion, is forced to "accept" edge lengths that are far less optimal (longer) than the asset graph, thus resulting in higher overall length for the tree. The same criterion also causes asset trees to be more fragile in structure when measured by the single-step survival ratio. Over longer time periods, in the beginning the asset graph decays more slowly than the asset tree, but in the long run the situation is reversed. The vertex degree distributions indicate that the possible scale free behavior of the asset graph is not as evident as it is in the case of the asset tree.}
}


@book{barabasi2016network,
  author = {Barab\'asi, Albert-L\'aszl\'o and P\'osfai, M\'arton},
  description = {Network Science: Albert-László Barabási: 9781107076266: Amazon.com: Books},
  interhash = {2dcba3ae6b58627716c5d2a63f7c0855},
  intrahash = {2a71231091413325c03aaed76c32a66b},
  isbn = {9781107076266 1107076269},
  keywords = {required sna snaseminar},
  publisher = {Cambridge University Press},
  refid = {958874494},
  timestamp = {2016-12-18T12:26:46.000+0100},
  title = {Network science},
  url = {http://barabasi.com/networksciencebook/},
  year = 2016
}






@book{luenberger2014investment,
  title={Investment Science},
  author={Luenberger, D. G.},
  isbn={9780199740086},
  lccn={2012047878},
  url={https://books.google.co.uk/books?id=YMSeDAEACAAJ},
  year={2014},
  publisher={Oxford University Press}
}

@book{campbell1997econometrics,
  title={The Econometrics of Financial Markets},
  author={Campbell, J. Y. and Campbell, J. W. and Lo, A. W. C. and MacKinlay, A. C. and Champbell, J. J. and LO, A. A. and MacKinlay, A. C. M. K. and Lo, P. A. W. and Campbell, O. E. P. A. E. J. Y.},
  isbn={9780691043012},
  lccn={96027868},
  url={https://books.google.co.uk/books?id=lkeKhnqUHx8C},
  year={1997},
  publisher={Princeton University Press}
}



@Book{Choudhry:2007,
  title={An Introduction to Value-at-Risk},
  author={Choudhry, M. and Tanna, K.},
  isbn={9780470033777},
  series={Securities Institute},
  year={2007},
  publisher={Wiley}
}



@article{1367-2630-12-8-085009,
  author={T. Aste and W Shaw and T. Di Matteo},
  title={Correlation structure and dynamics in volatile markets},
  journal={New Journal of Physics},
  volume={12},
  number={8},
  pages={085009},
  url={http://stacks.iop.org/1367-2630/12/i=8/a=085009},
  year={2010},
  abstract={The statistical signatures of the 'credit crunch' financial crisis that unfolded between 2008 and 2009 are investigated by combining tools from statistical physics and network theory. We devise measures for the collective behavior of stock prices based on the construction of topologically constrained graphs from cross-correlation matrices. We test the stability, statistical significance and economic meaningfulness of these graphs. The results show an intriguing trend that highlights a consistently decreasing centrality of the financial sector over the last 10 years.}
}

@article{liu2012sentiment,
  title={Sentiment analysis and opinion mining},
  author={Liu, B.},
  journal={Synthesis lectures on human language technologies},
  volume={5},
  number={1},
  pages={1--167},
  year={2012},
  publisher={Morgan \& Claypool Publishers}
}

@Manual{R2015,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2014},
    url = {http://www.R-project.org/},
  }


@article {JOFI:JOFI518,
author = {Fama, E. F.},
title = {EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK},
journal = {The Journal of Finance},
volume = {25},
number = {2},
publisher = {Blackwell Publishing Ltd},
issn = {1540-6261},
url = {http://dx.doi.org/10.1111/j.1540-6261.1970.tb00518.x},
doi = {10.1111/j.1540-6261.1970.tb00518.x},
pages = {383--417},
year = {1970},
}

@article{wrap54797,
          volume = {Volume 3},
          number = {Article number 1684},
           month = {April},
          author = {Preis, Tobias and Moat, Helen Susannah and Stanley, H. Eugene},
           title = {Quantifying trading behavior in financial markets using Google Trends},
       publisher = {Nature Publishing Group},
            year = {2013},
         journal = {Scientific Reports},
           pages = {Article number 1684}
		   }

@article{wrap50936,
          volume = {Vol.368},
          number = {No.1933},
          author = {Preis, Tobias and Reith, D. and Stanley, H. Eugene},
           title = {Complex dynamics of our economic life on different scales : insights from search engine query data},
       publisher = {The Royal Society Publishing},
         journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
           pages = {5707--5719},
            year = {2010}
}

@article{wrap54525,
          volume = {Volume 3},
           month = {May},
          author = {H. S. Moat and C. Curme and A. Avakian and D. Y. Kenett and H. E. Stanley and T. Preis},
           title = {Quantifying wikipedia usage patterns before stock market moves},
       publisher = {Nature Publishing Group},
         journal = {Scientific Reports},
           pages = {Article number 1801},
            year = {2013}}
@article{Mocanu2015,
title = "Collective attention in the age of (mis)information ",
journal = "Computers in Human Behavior ",
volume = "",
number = "0",
pages = " - ",
year = "2015",
note = "",
issn = "0747-5632",
doi = "http://dx.doi.org/10.1016/j.chb.2015.01.024",
author = "Delia Mocanu and Luca Rossi and Qian Zhang and Marton Karsai and Walter Quattrociocchi"
}

@article{BessiZVSCQ15,
  author    = {Alessandro Bessi and
               Fabiana Zollo and
               Michela Del Vicario and
               Antonio Scala and
               Guido Caldarelli and
               Walter Quattrociocchi},
  title     = {Trend of Narratives in the Age of Misinformation},
  journal   = {CoRR},
  volume    = {abs/1504.05163},
  year      = {2015},
  url       = {http://arxiv.org/abs/1504.05163},
  timestamp = {Sat, 02 May 2015 17:50:32 +0200}
}

@article{Quattrociocchi.0118093,
    author = {Bessi, Alessandro AND Coletto, Mauro AND Davidescu, George Alexandru AND Scala, Antonio AND Caldarelli, Guido AND Quattrociocchi, Walter},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Science vs Conspiracy: Collective Narratives in the Age of Misinformation},
    year = {2015},
    month = {02},
    volume = {10},
    pages = {e0118093},
    abstract = {<p>The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called <italic>collective intelligence</italic> unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.</p>},
    number = {2},
    doi = {10.1371/journal.pone.0118093}
}

@inproceedings{BessiPVZASCQ15,
  author    = {Alessandro Bessi and
               Fabio Petroni and
               Michela Del Vicario and
               Fabiana Zollo and
               Aris Anagnostopoulos and
               Antonio Scala and
               Guido Caldarelli and
               Walter Quattrociocchi},
  title     = {Viral Misinformation: The Role of Homophily and Polarization},
  booktitle = {Proceedings of the 24th International Conference on World Wide Web
               Companion, {WWW} 2015, Florence, Italy, May 18-22, 2015 - Companion
               Volume},
  pages     = {355--356},
  year      = {2015}
}



@article{ZolloNVBMSCQ15,
  author    = {Fabiana Zollo and
               Petra Kralj Novak and
               Michela Del Vicario and
               Alessandro Bessi and
               Igor Mozetic and
               Antonio Scala and
               Guido Caldarelli and
               Walter Quattrociocchi},
  title     = {Emotional Dynamics in the Age of Misinformation},
  journal   = {CoRR},
  volume    = {abs/1505.08001},
  year      = {2015},
  url       = {http://arxiv.org/abs/1505.08001},
  timestamp = {Mon, 01 Jun 2015 14:13:54 +0200}
}

@article{granger1980testing,
  title={Testing for causality: a personal viewpoint},
  author={Granger, Clive WJ},
  journal={Journal of Economic Dynamics and control},
  volume={2},
  pages={329--352},
  year={1980},
  publisher={Elsevier}
}
@article{PreisCurme:2014,
author = {Curme, Chester and Preis, Tobias and Stanley, H. Eugene and Moat, Helen Susannah},
title = {Quantifying the semantics of search behavior before stock market moves},
volume = {111},
number = {32},
pages = {11600-11605},
year = {2014},
doi = {10.1073/pnas.1324054111},
abstract ={Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.},
URL = {http://www.pnas.org/content/111/32/11600.abstract},
eprint = {http://www.pnas.org/content/111/32/11600.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}


@incollection{Wiener56,
    author = {Wiener, N.},
    booktitle = {Modern mathematics for engineers},
    chapter = {8},
    editor = {Beckenbach, E. F.},
    publisher = {McGraw-Hill, New York},
    title = {{The theory of prediction}},
    year = {1956}
}


@article{Das2005,
    author = {Das, Sanjiv Ranjan and Sisk, Jacob},
    journal = {The Journal of Portfolio Management},
    title = {{Financial Communities}},
    volume = {31},
	number = {4},
	pages = {112-123},
    year = {2005}
}

@incollection{Darbellay1998,
year={1998},
isbn={978-1-4612-7273-1},
booktitle={Signal Analysis and Prediction},
series={Applied and Numerical Harmonic Analysis},
editor={Prochazka, Ales and Uhlir, Jan and Rayner, P.W.J. and Kingsbury, N.G.},
doi={10.1007/978-1-4612-1768-8_18},
title={Predictability: An Information-Theoretic Perspective},
publisher={Birkhauser Boston},
author={Darbellay, GeorgesA.},
pages={249-262},
language={English}
}


@article{Banerjee:2011,
   title={Automated Analysis of News to Compute Market Sentiment: Its Impact on Liquidity and Trading},
   author={Banerjee, A. and diBartolomeo, D. and Mitra, G. and Yu, X.},
   publisher={UK Government's Foresight Project, The Future of Computer Trading in Financial Markets},
   year={2011}
}


@book{Mitra:2011,
  title={The Handbook of News Analytics in Finance},
  author={Mitra, G. and Mitra, L.},
  isbn={9781119990802},
  series={The Wiley Finance Series},
  year={2011},
  publisher={Wiley}
}

@book{Leinweber:2009,
  title={Nerds on Wall Street: Math, Machines and Wired Markets},
  author={Leinweber, D. J. and Aronson, T. R.},
  isbn={9780470500569},
  year={2009},
  publisher={Wiley}
}

@incollection{Dzielinski:2011,
  title={Volatility asymmetry, news, and private investors},
  author={Dzielinski, M. and Rieger, M. O. and Talpsepp, T.},
  year={2011},
  booktitle={The Handbook of News Analytics in Finance},
  publisher={Wiley Finance},
}

@article{Das:2007,
 author = {Das, Sanjiv R. and Chen, Mike Y.},
 title = {Yahoo! For Amazon: Sentiment Extraction from Small Talk on the Web},
 journal = {Manage. Sci.},
 issue_date = {September 2007},
 volume = {53},
 number = {9},
 month = sep,
 year = {2007},
 issn = {0025-1909},
 pages = {1375--1388},
 numpages = {14},
 publisher = {INFORMS},
}

@article{Moniz:2009,
 author = {Moniz, A. and Brar, G. and Davies, C.},
 title = {Have \uppercase{I} got news for you.},
 journal = {MacQuarie Research Report},
 year = {2009},
}

@article{Cahan:2009,
 author = {Cahan, R. and Jussa, J. and Luo, Y.},
 title = {Breaking news: how to use sentiment to pick stocks.},
 journal = {MacQuarie Research Report},
 year = {2009},
}

@article{diBartolomeo:2009,
   title={Equity portfolio risk (volatility) estimation using market information and sentiment},
   author={diBartolomeo, D. and Mitra, G. and Mitra, L.},
   publisher={Quantitative Finance},
   year={2009}
}

@incollection{diBartolomeo:2005,
  title={Making covariance based portfolio risk models sensitive to the rate at which markets reflect new information},
  author={diBartolomeo, D. and Warrick, S.},
  year={2005},
  booktitle={Linear Factor models},
  publisher={Elsevier Finance},
}

@book{Shefrin:2008,
  title={A Behavioral Approach to Asset Pricing},
  author={Shefrin, H.},
  isbn={9780080482248},
  lccn={2008020721},
  series={Academic Press Advanced Finance},
  year={2008},
  publisher={Elsevier Science}
}

@Article{Kahneman:1979,
  author={Kahneman, D. and Tversky, A.},
  title={Prospect Theory: An Analysis of Decision under Risk},
  journal={Econometrica},
  year={1979},
  volume={47},
  number={2},
  pages={263-91},
  month={March},
}


@incollection{Das:2010,
  title={News Analytics: Framework, Techniques and Metrics},
  author={Das, S. R.},
  year={2010},
  booktitle={The Handbook of News Analytics in Finance},
  publisher={Wiley Finance},

}

@article{Patton:2009,
   title={Does Beta Move with News? Firm-Specific Information Flows and Learning About Profitability},
   author={Patton, A. and Verardo, M.},
   publisher={FMG Discussion Papers available from http://eprints.lse.ac.uk/24421/1/dp630.pdf},
   year={2009}
}

@book{Banerjee1:2011,
title = {Impact of information arrival on volatility of intraday stock returns},
series = {Working paper series : WPS / Indian Institute of Management Calcutta},
author = {Banerjee, A. and Paul, S. and Hazra, S. and Dalmia, R.},
publisher = {Calcutta : IIMC},
year = {2011},
}

@article{Bollen20111,
title = "\uppercase{t}witter mood predicts the stock market ",
journal = "Journal of Computational Science ",
volume = "2",
number = "1",
pages = "1 - 8",
year = "2011",
note = "",
issn = "1877-7503",
author = "J. Bollen and H. Mao and X. Zeng",
}


@ARTICLE{Weiss1999MaximizingTP,
author={S. M. Weiss and C. Apte and F. J. Damerau and D. E. Johnson and F. J. Oles and T. Goetz and T. Hampp},
journal={IEEE Intelligent Systems and their Applications},
title={Maximizing text-mining performance},
year={1999},
volume={14},
number={4},
pages={63-69},
keywords={data mining;software performance evaluation;adaptive systems;sampling methods;decision trees;dictionaries;electronic mail;data warehouses;full-text databases;text mining;performance maximization;adaptive resampling approach;decision trees;pooled local dictionaries;Reuters-21578 benchmark data;customer electronic mail routing system;Decision trees;Dictionaries;Classification tree analysis;Prediction methods;History;Mood;Degradation;Testing;Nearest neighbor searches;Humans},
doi={10.1109/5254.784086},
ISSN={1094-7167},
month={July},}

@inproceedings{Dumais:1998:ILA:288627.288651,
 author = {Dumais, Susan and Platt, John and Heckerman, David and Sahami, Mehran},
 title = {Inductive Learning Algorithms and Representations for Text Categorization},
 booktitle = {Proceedings of the Seventh International Conference on Information and Knowledge Management},
 series = {CIKM '98},
 year = {1998},
 isbn = {1-58113-061-9},
 location = {Bethesda, Maryland, USA},
 pages = {148--155},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/288627.288651},
 doi = {10.1145/288627.288651},
 acmid = {288651},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {classification, information management, machine learning, support vector machines, text categorization},
}

@inproceedings{Pang:2002:TUS:1118693.1118704,
 author = {Pang, B. and Lee, L. and Vaithyanathan, S.},
 title = {Thumbs Up?: Sentiment Classification Using Machine Learning Techniques},
 booktitle = {Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10},
 series = {EMNLP '02},
 year = {2002},
 pages = {79--86},
 numpages = {8},
 url = {https://doi.org/10.3115/1118693.1118704},
 doi = {10.3115/1118693.1118704},
 acmid = {1118704},
 publisher = {Association for Computational Linguistics},
 address = {Stroudsburg, PA, USA},
}

@inproceedings{Morinaga:2002:MPR:775047.775098,
 author = {Morinaga, S. and Yamanishi, K. and Tateishi, K. and Fukushima, T.},
 title = {Mining Product Reputations on the Web},
 booktitle = {Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '02},
 year = {2002},
 isbn = {1-58113-567-X},
 location = {Edmonton, Alberta, Canada},
 pages = {341--349},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/775047.775098},
 doi = {10.1145/775047.775098},
 acmid = {775098},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@book{Salton:1986:IMI:576628,
 author = {Salton, G. and McGill, M. J.},
 title = {Introduction to Modern Information Retrieval},
 year = {1986},
 isbn = {0070544840},
 publisher = {McGraw-Hill, Inc.},
 address = {New York, NY, USA},
}

@INPROCEEDINGS{Das01yahoo!for,
    author = {S. R. Das and M. Y. Chen and T. V. Agarwal and C. Brooks and Y. Chan and D. Gibson and D. Leinweber and A. Martinez-jerez and P. Raghubir and S. Rajagopalan and A. Ranade and M. Rubinstein and P. Tufano},
    title = {Yahoo! for amazon: Sentiment extraction from small talk on the web},
    booktitle = {8th Asia Pacific Finance Association Annual Conference},
    year = {2001}
}


@techreport{NBERw20010,
 title = "Using Social Media to Measure Labor Market Flows",
 author = "Antenucci, D. and Cafarella, M. and Levenstein, M. and Ré, C. and Shapiro, M. D.",
 institution = "National Bureau of Economic Research",
 type = "Working Paper",
 series = "Working Paper Series",
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 year = "2014",
 month = "March",
 doi = {10.3386/w20010},
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}


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note = "Smart Business Networks: Concepts and Empirical Evidence ",
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journal={Social Network Analysis and Mining},
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number={3},
title={Mining dynamic social networks from public news articles for company value prediction},
publisher={Springer Vienna},
author={Jin, Yingzi and Lin, Ching-Yung and Matsuo, Yutaka and Ishizuka, Mitsuru},
pages={217-228},
}











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@Article{e16042309,
AUTHOR = {Zaremba, Anna and Aste, Tomaso},
TITLE = {Measures of Causality in Complex Datasets with Application to Financial Data},
JOURNAL = {Entropy},
VOLUME = {16},
YEAR = {2014},
NUMBER = {4},
PAGES = {2309--2349},
URL = {http://www.mdpi.com/1099-4300/16/4/2309},
ISSN = {1099-4300},
ABSTRACT = {This article investigates the causality structure of financial time series. We concentrate on three main approaches to measuring causality: linear Granger causality, kernel generalisations of Granger causality (based on ridge regression and the Hilbert–Schmidt norm of the cross-covariance operator) and transfer entropy, examining each method and comparing their theoretical properties, with special attention given to the ability to capture nonlinear causality. We also present the theoretical benefits of applying non-symmetrical measures rather than symmetrical measures of dependence. We apply the measures to a range of simulated and real data. The simulated data sets were generated with linear and several types of nonlinear dependence, using bivariate, as well as multivariate settings. An application to real-world financial data highlights the practical difficulties, as well as the potential of the methods. We use two real data sets: (1) U.S. inflation and one-month Libor; (2) S&amp;P data and exchange rates for the following currencies: AUDJPY, CADJPY, NZDJPY, AUDCHF, CADCHF, NZDCHF. Overall, we reach the conclusion that no single method can be recognised as the best in all circumstances, and each of the methods has its domain of best applicability. We also highlight areas for improvement and future research.},
DOI = {10.3390/e16042309}
}




@Article{e15010113,
AUTHOR = {Amblard, Pierre-Olivier and Michel, Olivier J. J.},
TITLE = {The Relation between Granger Causality and Directed Information Theory: A Review},
JOURNAL = {Entropy},
VOLUME = {15},
YEAR = {2013},
NUMBER = {1},
PAGES = {113--143},
URL = {http://www.mdpi.com/1099-4300/15/1/113},
ISSN = {1099-4300},
ABSTRACT = {This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory. The definitions of Granger causality based on prediction are recalled, and the importance of the observation set is discussed. We present the definitions based on conditional independence. The notion of instantaneous coupling is included in the definitions. The concept of Granger causality graphs is discussed. We present directed information theory from the perspective of studies of causal influences between stochastic processes. Causal conditioning appears to be the cornerstone for the relation between information theory and Granger causality. In the bivariate case, the fundamental measure is the directed information, which decomposes as the sum of the transfer entropies and a term quantifying instantaneous coupling. We show the decomposition of the mutual information into the sums of the transfer entropies and the instantaneous coupling measure, a relation known for the linear Gaussian case. We study the multivariate case, showing that the useful decomposition is blurred by instantaneous coupling. The links are further developed by studying how measures based on directed information theory naturally emerge from Granger causality inference frameworks as hypothesis testing.},
DOI = {10.3390/e15010113}
}



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  year={2013},
  organization={IEEE}
}



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keywords = "economic indicator prediction",
keywords = "Web buzz analysis",
keywords = "coolhunting "
}

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year = {2014},
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URL = {
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},
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}

}

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@article{Tobias:2013,
 author = {Alanyali, M. and Moat, H. S. and Preis, T.},
 title = {Quantifying the Relationship Between Financial News and the Stock Market},
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year = {2013},
 volume = {3}
}

@Article{Timm:2014,
  author={Timm O. Sprenger and Philipp G. Sandner and Andranik Tumasjan and Isabell M. Welpe},
  title={{News or Noise? Using \uppercase{t}witter to Identify and Understand Company-specific News Flow}},
  journal={Journal of Business Finance \& Accounting},
  year=2014,
  volume={41},
  number={7-8},
  pages={791-830},
  month={09},
}

@article{Patton01092012,
author = {Patton, Andrew J. and Verardo, Michela},
title = {Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability},
volume = {25},
number = {9},
pages = {2789-2839},
year = {2012},
doi = {10.1093/rfs/hhs073},
journal = {Review of Financial Studies}
}

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}


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}

@article{tetlock2008more,
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}

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   year={2013}
}

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    year = {2014}
}

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publisher={Working Paper}
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  year={2011},
  publisher={Wiley}
}

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author = {Steve Y. Y. and S. Y. K. Mo and X. Zhu},
title = {An Empirical Study of the Financial Community Network on \uppercase{t}witter},
year = {2014},
journal = {2014 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics}
}


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@article{Sun2016,
abstract = {•Transitions between patterns are emergent properties in spatial epidemics.•Two types of pattern transitions in infectious diseases are shown.•We provide possible mechanisms of pattern transition in spatial epidemics.•Pattern transition promotes complexity in spatial epidemics.•The results are applicable in medical science, ecology, quantitative finance and so on.},
affiliation = {Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, PR China; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Department of Vector Ecology and Environment, Nagasaki University Institute of Tropical Medicine (NEKKEN), Nagasaki 852-8523, Japan; Center of Mathematics for Social Creativity, Hokkaido University, Sapporo 060-0812, Japan; Department of Mathematics, Southeast University, Nanjing 210096, PR China; Department of Mathematics and Statistics, University of Victoria, Victoria BC V8W 3R4, Canada; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan},
author = {Sun, Gui-Quan and Jusup, Marko and Jin, Zhen and Wang, Yi and Wang, Zhen},
doi = {10.1016/j.plrev.2016.08.002},
journal = {Physics of Life Reviews},
keywords = {Reaction–diffusion equation; Cellular automata; Spatial heterogeneity; Seasonality and noise; Coherence resonance; Cyclic evolution},
language = {English},
number = {Complete},
pages = {43-73},
title = {Review},
volume = {19},
year = {2016},
}

@article{DBLP:journals/amc/Li15a,
  author    = {Li Li},
  title     = {Patch invasion in a spatial epidemic model},
  journal   = {Applied Mathematics and Computation},
  volume    = {258},
  pages     = {342--349},
  year      = {2015},
  url       = {http://dx.doi.org/10.1016/j.amc.2015.02.006},
  doi       = {10.1016/j.amc.2015.02.006},
  timestamp = {Sat, 21 Mar 2015 13:18:42 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/amc/Li15a},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}


@article{Sun20141507,
title = "Influence of time delay and nonlinear diffusion on herbivore outbreak ",
journal = "Communications in Nonlinear Science and Numerical Simulation ",
volume = "19",
number = "5",
pages = "1507 - 1518",
year = "2014",
note = "",
issn = "1007-5704",
doi = "http://doi.org/10.1016/j.cnsns.2013.09.016",
url = "http://www.sciencedirect.com/science/article/pii/S1007570413004164",
author = "Gui-Quan Sun and Amit Chakraborty and Quan-Xing Liu and Zhen Jin and Kurt E. Anderson and Bai-Lian Li",
keywords = "Herbivore-plant",
keywords = "Time delay",
keywords = "Spatial diffusion",
keywords = "Outbreak",
keywords = "Synchrony "
}


@article{PhysRevE.90.042807,
  title = {Nonlinear growth and condensation in multiplex networks},
  author = {Nicosia, V. and Bianconi, G. and Latora, V. and Barthelemy, M.},
  journal = {Phys. Rev. E},
  volume = {90},
  issue = {4},
  pages = {042807},
  numpages = {13},
  year = {2014},
  month = {Oct},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.90.042807},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.90.042807}
}

@Article{Lacasa2015,
author={Lacasa, Lucas
and Nicosia, Vincenzo
and Latora, Vito},
title={Network structure of multivariate time series},
journal={Scientific Reports},
year={2015},
month={Oct},
day={21},
publisher={The Author(s) SN  -},
volume={5},
pages={15508 EP  -},
note={Article},
url={http://dx.doi.org/10.1038/srep15508}
}



@article{Lü20111150,
title = "Link prediction in complex networks: A survey ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "390",
number = "6",
pages = "1150 - 1170",
year = "2011",
note = "",
issn = "0378-4371",
doi = "http://doi.org/10.1016/j.physa.2010.11.027",
url = "http://www.sciencedirect.com/science/article/pii/S037843711000991X",
author = "Linyuan Lü and Tao Zhou",
keywords = "Link prediction",
keywords = "Complex networks",
keywords = "Node similarity",
keywords = "Maximum likelihood methods",
keywords = "Probabilistic models "
}




@article{1367-2630-17-7-073029,
  author={Emanuele Cozzo and Mikko Kivelä and Manlio De Domenico and Albert Solé-Ribalta and Alex Arenas and Sergio Gómez and Mason A
Porter and Yamir Moreno},
  title={Structure of triadic relations in multiplex networks},
  journal={New Journal of Physics},
  volume={17},
  number={7},
  pages={073029},
  url={http://stacks.iop.org/1367-2630/17/i=7/a=073029},
  year={2015},
  abstract={Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.}
}


@article{doi:10.1108/IMDS-12-2017-0582,
author = {Mengdi Li and Eugene Ch’ng and Alain Yee Loong Chong and Simon See},
title = {Multi-class Twitter sentiment classification with emojis},
journal = {Industrial Management \& Data Systems},
volume = {118},
number = {9},
pages = {1804-1820},
year = {2018},
doi = {10.1108/IMDS-12-2017-0582},

URL = {
        https://doi.org/10.1108/IMDS-12-2017-0582

},
eprint = {
        https://doi.org/10.1108/IMDS-12-2017-0582

}
}

@InProceedings{10.1007/978-3-030-01159-8_26,
author="Sidorov, Sergei P.
and Faizliev, Alexey R.
and Levshunov, Michael
and Chekmareva, Alfia
and Gudkov, Alexander
and Korobov, Eugene",
editor="Staab, Steffen
and Koltsova, Olessia
and Ignatov, Dmitry I.",
title="Graph-Based Clustering Approach for Economic and Financial Event Detection Using News Analytics Data",
booktitle="Social Informatics",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="271--280",
abstract="In recent years, one of the most extensive research topics in social media analysis has been event detection. Most of the approaches use fixed temporal and spatial resolutions to detect events. In this paper, we employ a procedure for the detection of economic and financial events using news analytics data. We use an algorithm to compute a data similarity graph at chosen scales and detect economic and financial events simultaneously by a single graph-based clustering process. Experimental results on real world data collected from news analytics providers demonstrate the effectiveness of the event detection procedure based on real-time news analytics data.",
isbn="978-3-030-01159-8"
}


@article{LI2018,
title = "Portfolio optimization based on network topology",
journal = "Physica A: Statistical Mechanics and its Applications",
year = "2018",
issn = "0378-4371",
doi = "https://doi.org/10.1016/j.physa.2018.10.014",
url = "http://www.sciencedirect.com/science/article/pii/S0378437118313529",
author = "Yan Li and Xiong-Fei Jiang and Yue Tian and Sai-Ping Li and Bo Zheng",
keywords = "Dynamic complex networks, Topological structures, Portfolio optimization, Econophysics"
}

@article{PhysRevLett.111.058702,
  title = {Coevolution and Correlated Multiplexity in Multiplex Networks},
  author = {Kim, Jung Yeol and Goh, K.-I.},
  journal = {Phys. Rev. Lett.},
  volume = {111},
  issue = {5},
  pages = {058702},
  numpages = {5},
  year = {2013},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.111.058702},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.111.058702}
}


@Article{Battiston2017,
author="Battiston, Federico
and Nicosia, Vincenzo
and Latora, Vito",
title="The new challenges of multiplex networks: Measures and models",
journal="The European Physical Journal Special Topics",
year="2017",
volume="226",
number="3",
pages="401--416",
abstract="What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than only by the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.",
issn="1951-6401",
doi="10.1140/epjst/e2016-60274-8",
url="http://dx.doi.org/10.1140/epjst/e2016-60274-8"
}


@article{SandovalJunior2012187,
title = "Correlation of financial markets in times of crisis ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "391",
number = "1–2",
pages = "187 - 208",
year = "2012",
note = "",
issn = "0378-4371",
doi = "http://dx.doi.org/10.1016/j.physa.2011.07.023",
url = "http://www.sciencedirect.com/science/article/pii/S037843711100570X",
author = "Leonidas Sandoval Junior and Italo De Paula Franca",
keywords = "Financial markets",
keywords = "Crisis",
keywords = "Correlation matrix",
keywords = "Random matrix theory "
}

@article{10.1371/journal.pone.0107056,
    author = {Tan, Fei AND Xia, Yongxiang AND Zhu, Boyao},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Link Prediction in Complex Networks: A Mutual Information Perspective},
    year = {2014},
    month = {09},
    volume = {9},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0107056},
    pages = {1-8},
    abstract = {Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.},
    number = {9},
    doi = {10.1371/journal.pone.0107056}
}

@article{PhysRevLett.86.3200,
  title = {Epidemic Spreading in Scale-Free Networks},
  author = {Pastor-Satorras, Romualdo and Vespignani, Alessandro},
  journal = {Phys. Rev. Lett.},
  volume = {86},
  issue = {14},
  pages = {3200--3203},
  numpages = {0},
  year = {2001},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.86.3200},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.86.3200}
}


@Inbook{Barabasi2003,
author="Barab{\'a}si, Albert-L{\'a}szl{\'o}
and Ravasz, Erzs{\'e}bet
and Oltvai, Zolt{\'a}n",
editor="Pastor-Satorras, Romualdo
and Rubi, Miguel
and Diaz-Guilera, Albert",
title="Hierarchical Organization of Modularity in Complex Networks",
bookTitle="Statistical Mechanics of Complex Networks",
year="2003",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="46--65",
isbn="978-3-540-44943-0",
doi="10.1007/978-3-540-44943-0_4",
url="http://dx.doi.org/10.1007/978-3-540-44943-0_4"
}


@article{0295-5075-97-6-68006,
  author={G. D'Agostino and A. Scala and V. Zlatić and G. Caldarelli},
  title={Robustness and assortativity for diffusion-like processes in scale-free networks},
  journal={EPL (Europhysics Letters)},
  volume={97},
  number={6},
  pages={68006},
  url={http://stacks.iop.org/0295-5075/97/i=6/a=68006},
  year={2012}
}

@article{10.1371/journal.pone.0031929,
    author = {Song, Won-Min AND Di Matteo, T. AND Aste, Tomaso},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Hierarchical Information Clustering by Means of Topologically Embedded Graphs},
    year = {2012},
    month = {03},
    volume = {7},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031929},
    pages = {1-14},
    abstract = {<p>We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.</p>},
    number = {3},
    doi = {10.1371/journal.pone.0031929}
}

@article{Tumminello26072005,
author = {Tumminello, M. and Aste, T. and Di Matteo, T. and Mantegna, R. N.},
title = {A tool for filtering information in complex systems},
volume = {102},
number = {30},
pages = {10421-10426},
year = {2005},
doi = {10.1073/pnas.0500298102},
abstract ={We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties.},
URL = {http://www.pnas.org/content/102/30/10421.abstract},
eprint = {http://www.pnas.org/content/102/30/10421.full.pdf},
journal = {Proceedings of the National Academy of Sciences of the United States of America}
}

@article{doi:10.1137/070710111,
author = {Aaron Clauset and Cosma Rohilla Shalizi and M. E. J. Newman},
title = {Power-Law Distributions in Empirical Data},
journal = {SIAM Review},
volume = {51},
number = {4},
pages = {661-703},
year = {2009},
doi = {10.1137/070710111},

URL = {
        http://dx.doi.org/10.1137/070710111

},
eprint = {
        http://dx.doi.org/10.1137/070710111

}

}

@article{PhysRevLett.104.108702,
  title = {Entropic Origin of Disassortativity in Complex Networks},
  author = {Johnson, Samuel and Torres, Joaqu\'{\i}n J. and Marro, J. and Mu\~noz, Miguel A.},
  journal = {Phys. Rev. Lett.},
  volume = {104},
  issue = {10},
  pages = {108702},
  numpages = {4},
  year = {2010},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.104.108702},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.104.108702}
}


@article{1742-5468-2012-07-P07025,
  author={Giacomo Livan and Jun-ichi Inoue and Enrico Scalas},
  title={On the non-stationarity of financial time series: impact on optimal portfolio selection},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  volume={2012},
  number={07},
  pages={P07025},
  url={http://stacks.iop.org/1742-5468/2012/i=07/a=P07025},
  year={2012},
  abstract={We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices.}
}


@article{PhysRevE.94.062306,
  title = {Parsimonious modeling with information filtering networks},
  author = {Barfuss, Wolfram and Massara, Guido Previde and Di Matteo, T. and Aste, Tomaso},
  journal = {Phys. Rev. E},
  volume = {94},
  issue = {6},
  pages = {062306},
  numpages = {12},
  year = {2016},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.94.062306},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.94.062306}
}


@ARTICLE{2016arXiv160207349B,
   author = {{Barfuss}, W. and {Previde Massara}, G. and {Di Matteo}, T. and
{Aste}, T.},
    title = "{Parsimonious modeling with Information Filtering Networks}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1602.07349},
 primaryClass = "cs.IT",
 keywords = {Computer Science - Information Theory, Statistics - Machine Learning},
     year = 2016,
    month = feb
}

@article{musmeci2014clustering,
  title={Clustering and hierarchy of financial markets data: advantages of the DBHT.},
  author={Musmeci, Nicol{\'o} and Aste, Tomaso and di Matteo, Tiziana},
  journal={CoRR},
  year={2014}
}

@article{Morales20136470,
title = "Non-stationary multifractality in stock returns ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "392",
number = "24",
pages = "6470 - 6483",
year = "2013",
note = "",
issn = "0378-4371",
doi = "http://dx.doi.org/10.1016/j.physa.2013.08.037",
url = "http://www.sciencedirect.com/science/article/pii/S0378437113007668",
author = "Raffaello Morales and T. Di Matteo and Tomaso Aste",
keywords = "Multifractality",
keywords = "Generalised Hurst exponent",
keywords = "Multifractal models "
}


@article{musmeci2016interplay,
  title={Interplay between past market correlation structure changes and future volatility outbursts},
  author={Musmeci, Nicol{\'o} and Aste, Tomaso and Di Matteo, T},
  journal={Scientific Reports},
  volume={6},
  pages={36320},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{pozzi2008centrality,
  title={Centrality and peripherality in filtered graphs from dynamical financial correlations},
  author={Pozzi, Francesco and Di Matteo, Tiziana and Aste, Tomaso},
  journal={Advances in Complex Systems},
  volume={11},
  number={06},
  pages={927--950},
  year={2008},
  publisher={World Scientific Publishing Company}
}

@book{aitchison1957lognormal,
  title={The Lognormal Distribution: With Special Reference to Its Uses in Economics},
  author={Aitchison, J. and Brown, J.A.C.},
  lccn={a58001106},
  series={Cambridge. University. Dept. of Applied Economics. Monographs, 5},
  year={1957},
  publisher={University Press}
}

@book{granger1992using,
  title={Using the Mutual Information Coefficient to Identify Lags in Non-linear Models},
  author={Granger, C.W.J. and Lin, J.L.},
  series={Discussion paper: Jingji-Yanjiusuo},
  year={1992},
  publisher={Institute of Economics, Academia Sinica}
}

@inproceedings{pozzi2008dynamical,
  title={Dynamical correlations in financial systems [6802-54]},
  author={Pozzi, F and Aste, T and Rotundo, G and Di Matteo, T},
  booktitle={PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING},
  volume={6802},
  pages={6802},
  year={2008},
  organization={International Society for Optical Engineering; 1999}
}

@article{918bf1f7d91649f59a1173af04bfc06a,
title = "Risk diversification: a study of persistence with a filtered correlation-network approach",
author = "Nicolo Musmeci and {Di Matteo}, Tiziana and Tomaso Aste",
year = "2015",
month = "3",
volume = "1",
pages = "1--22",
journal = "Journal of Network Theory in Finance",
number = "1",

}

@Article{Zheng2012,
author={Zheng, Zeyu
and Podobnik, Boris
and Feng, Ling
and Li, Baowen},
title={Changes in Cross-Correlations as an Indicator for Systemic Risk},
year={2012},
month={Nov},
day={26},
volume={2},
issn={2045-2322},
journal={Scientific reports},
  publisher={Nature Publishing Group},
pages = {Article number 888}
}



@article{PhysRevE.88.012806,
  title = {Evolution of correlation structure of industrial indices of U.S. equity markets},
  author = {Buccheri, Giuseppe and Marmi, Stefano and Mantegna, Rosario N.},
  journal = {Phys. Rev. E},
  volume = {88},
  issue = {1},
  pages = {012806},
  numpages = {7},
  year = {2013},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.88.012806},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.88.012806}
}


@article{morales2012dynamical,
  title={Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series},
  author={Morales, Raffaello and Di Matteo, Tiziana and Gramatica, Ruggero and Aste, Tomaso},
  journal={Physica A: Statistical Mechanics and its Applications},
  volume={391},
  number={11},
  pages={3180--3189},
  year={2012},
  publisher={North-Holland}
}

@article{aste2010correlation,
  title={Correlation structure and dynamics in volatile markets},
  author={Aste, Tomaso and Shaw, W. and Di Matteo, Tiziana},
  journal={New Journal of Physics},
  volume={12},
  number={8},
  pages={085009},
  year={2010},
  publisher={IOP Publishing}
}

@inproceedings{pozzi2009use,
  title={The use of topological quantities to detect hierarchical properties in financial markets: the Financial sector in NYSE},
  author={Pozzi, Francesco and Aste, Tomaso and Shaw, William and Di Matteo, Tiziana and Mastorakis, NE and Croitoru, A and Balas, VE and Son, E and Mladenov, V},
  booktitle={WSEAS International Conference. Proceedings. Recent Advances in Computer Engineering},
  number={10},
  year={2009},
  organization={WSEAS}
}




@article{pozzi2013spread,
  title={Spread of risk across financial markets: better to invest in the peripheries},
  author={Pozzi, Francesco and Di Matteo, Tiziana and Aste, Tomaso},
  journal={Scientific reports},
  volume={3},
  year={2013},
  publisher={Nature Publishing Group}
}


@article{Battiston06092016,
author = {Battiston, Stefano and Caldarelli, Guido and May, Robert M. and Roukny, Tarik and Stiglitz, Joseph E.},
title = {The price of complexity in financial networks},
volume = {113},
number = {36},
pages = {10031-10036},
year = {2016},
doi = {10.1073/pnas.1521573113},
URL = {http://www.pnas.org/content/113/36/10031.abstract},
eprint = {http://www.pnas.org/content/113/36/10031.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}


@article{10.1371/journal.pone.0109462,
    author = {Montalto, Alessandro AND Faes, Luca AND Marinazzo, Daniele},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy},
    year = {2014},
    month = {10},
    volume = {9},
    url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0109462},
    pages = {e109462},
    number = {10},
    doi = {10.1371/journal.pone.0109462}
}

@Manual{R2015,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2014},
    url = {http://www.R-project.org/},
  }


@article{Baghli2006380,
title = "A model-free characterization of causality ",
journal = "Economics Letters ",
volume = "91",
number = "3",
pages = "380 - 388",
year = "2006",
note = "",
issn = "0165-1765",
doi = "http://dx.doi.org/10.1016/j.econlet.2005.12.016",
url = "http://www.sciencedirect.com/science/article/pii/S0165176505004234",
author = "Mustapha Baghli",
keywords = "Causality in information",
keywords = "Information-theoretic statistics",
keywords = "Nonlinearity",
keywords = "Fast double bootstrap test "
}


@incollection{1507.00784,
  author      = {T. T. P. Souza and O. Kolchyna and P. Treleaven and T. Aste},
  title       = "\uppercase{t}witter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry",
  editor      = "Gautam Mitra and Xiang Yu",
  booktitle   = "Handbook of Sentiment Analysis in Finance",
  year        = 2016,
  chapter     = 23
}

@article{Barberis1998307,
title = "A model of investor sentiment ",
journal = "Journal of Financial Economics ",
volume = "49",
number = "3",
pages = "307 - 343",
year = "1998",
note = "",
issn = "0304-405X",
doi = "http://dx.doi.org/10.1016/S0304-405X(98)00027-0",
url = "http://www.sciencedirect.com/science/article/pii/S0304405X98000270",
author = "Nicholas Barberis and Andrei Shleifer and Robert Vishny",
keywords = "Investor sentiment",
keywords = "Underreaction",
keywords = "Overreaction "
}


@article{lizier2010differentiating,
  title={Differentiating information transfer and causal effect},
  author={Lizier, Joseph T. and Prokopenko, Mikhail},
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  pages={605--615},
  year={2010},
  publisher={Springer}
}



@article{Huang01032015,
author = {Huang, Dashan and Jiang, Fuwei and Tu, Jun and Zhou, Guofu},
title = {Investor Sentiment Aligned: A Powerful Predictor of Stock Returns},
volume = {28},
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pages = {791-837},
year = {2015},
doi = {10.1093/rfs/hhu080},
journal = {Review of Financial Studies}
}


@inproceedings{Sehgal:2007:SSP:1335998.1336036,
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 series = {ICDMW '07},
 year = {2007},
 isbn = {0-7695-3033-8},
 pages = {21--26},
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 url = {http://dx.doi.org/10.1109/ICDMW.2007.97},
 doi = {10.1109/ICDMW.2007.97},
 acmid = {1336036},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
}

@INPROCEEDINGS{Zhang_tradingstrategies,
    author = {Wenbin Zhang and Steven Skiena},
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    year = {2010}
}


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            year = {2013}
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pages = {2023--2052},
year = {2009},
}


@article{doi:10.1080/14697688.2015.1039865,
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pages = {1405-1416},
year = {2015},
doi = {10.1080/14697688.2015.1039865},

URL = {
        http://dx.doi.org/10.1080/14697688.2015.1039865

},
eprint = {
        http://dx.doi.org/10.1080/14697688.2015.1039865

}

}



@article{Doukas:2009,
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pages = {23-43},
year = {2009},
doi = {10.1108/19405979200900002}
}

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Stock Message Boards},
  volume = 59,
  year = 2004
}



@article{mao2014quantifying,
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  year={2014}
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year={1998},
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doi={10.1007/978-1-4612-1768-8_18},
title={Predictability: An Information-Theoretic Perspective},
publisher={Birkhauser Boston},
author={Darbellay, GeorgesA.},
pages={249-262},
language={English}
}


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year={2011},
issn={0929-5313},
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volume={30},
number={1},
doi={10.1007/s10827-010-0262-3},
title={Transfer entropy—a model-free measure of effective connectivity for the neurosciences},
url={http://dx.doi.org/10.1007/s10827-010-0262-3},
publisher={Springer US},
author={Vicente, Raul and Wibral, Michael and Lindner, Michael and Pipa, Gordon},
pages={45-67},
language={English}
}



@incollection{Dzeroski:2014,
year={2014},
isbn={978-3-319-11811-6},
booktitle={Discovery Science},
volume={8777},
series={Lecture Notes in Computer Science},
editor={D\u{z}eroski, Sa\u{s}o and Panov, Pan\u{c}e and Kocev, Dragi and Todorovski, Ljup\u{c}o},
doi={10.1007/978-3-319-11812-3_2},
title={Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market},
url={http://dx.doi.org/10.1007/978-3-319-11812-3_2},
publisher={Springer International Publishing},
keywords={Wrapper feature selection; Bayesian Networks; Stock microblogging sentiment},
author={Al Nasseri, Alya and Tucker, Allan and de Cesare, Sergio},
pages={13-24}
}

@Article {RePEc:dur:durham:2011_06,
title = {Media Sentiment and UK Stock Returns},
journal = {Working Papers},
year = {2011},
author = {Nicky J. Ferguson and Jie Michael Guo and Nicky Herbert Y.T. Lam and Dennis Philip},
publisher = {Durham University Business School}
}

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    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {The Effects of \uppercase{t}witter Sentiment on Stock Price Returns},
    year = {2015},
    month = {09},
    volume = {10},
    url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0138441},
    pages = {e0138441},
    number = {9},
    doi = {10.1371/journal.pone.0138441}
}

@Article{ICWSM101513,
author = {Eric Gilbert and Karrie Karahalios},
title = {Widespread Worry and the Stock Market},
conference = {International AAAI Conference on Web and Social Media},
year = {2010},
pages = {58--65},
url = {https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1513/1833}
}

@incollection{Olaniyan:2015,
year={2015},
isbn={978-3-319-17090-9},
booktitle={Statistical Learning and Data Sciences},
volume={9047},
series={Lecture Notes in Computer Science},
editor={Gammerman, Alexander and Vovk, Vladimir and Papadopoulos, Harris},
doi={10.1007/978-3-319-17091-6_15},
title={Social Web-Based Anxiety Index's Predictive Information on S\&P 500 Revisited},
url={http://dx.doi.org/10.1007/978-3-319-17091-6\_15},
publisher={Springer International Publishing},
author={Olaniyan, Rapheal and Stamate, Daniel and Logofatu, Doina},
pages={203-213},
language={English}
}

@Article {2011arXiv1112.1051M,
   author = {Mao, H. and Counts, S. and Bollen, J.},
    title = {Predicting Financial Markets: Comparing Survey, News, \uppercase{t}witter and Search Engine Data},
  journal = {ArXiv e-prints. https://arxiv.org/pdf/1112.1051},
    year = {2011}
}

@article{DBLP:journals/eswa/NasseriTC15,
  author    = {Alya Al Nasseri and
               Allan Tucker and
               Sergio de Cesare},
  title     = {Quantifying StockTwits semantic terms' trading behavior in financial
               markets: An effective application of decision tree algorithms},
  journal   = {Expert Syst. Appl.},
  volume    = {42},
  number    = {23},
  pages     = {9192--9210},
  year      = {2015},
  url       = {http://dx.doi.org/10.1016/j.eswa.2015.08.008},
  doi       = {10.1016/j.eswa.2015.08.008}
}


@Article {HestonRanjan:2014,
   author = {Heston, Steven L. and Sinha, Nitish Ranjan},
    title = {News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns},
  journal = {Robert H. Smith School Research Paper},
  URL = {http://ssrn.com/abstract=2311310},
    year = {2014}
}

@article{citeulike:12299800,
    author = {Preis, T. and Moat, H. S. and Stanley, H. E.},
    day = {25},
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    issn = {2045-2322},
    journal = {Scientific Reports},
    month = apr,
    title = {{Quantifying Trading Behavior in Financial Markets Using Google Trends}},
    url = {http://dx.doi.org/10.1038/srep01684},
    volume = {3},
    year = {2013}
}


@inbook{Cont2005,
author="Cont, Rama",
editor="L{\'e}vy-V{\'e}hel, Jacques
and Lutton, Evelyne",
title="Long range dependence in financial markets",
bookTitle="Fractals in Engineering: New Trends in Theory and Applications",
year="2005",
publisher="Springer London",
address="London",
pages="159--179",
isbn="978-1-84628-048-1",
doi="10.1007/1-84628-048-6_11",
url="http://dx.doi.org/10.1007/1-84628-048-6_11"
}


@article{e15051643,
AUTHOR = {Wang, Gang-Jin and Xie, Chi and Chen, Yi-Jun and Chen, Shou},
TITLE = {Statistical Properties of the Foreign Exchange Network at Different Time Scales: Evidence from Detrended Cross-Correlation Coefficient and Minimum Spanning Tree},
JOURNAL = {Entropy},
VOLUME = {15},
YEAR = {2013},
NUMBER = {5},
PAGES = {1643},
URL = {http://www.mdpi.com/1099-4300/15/5/1643},
ISSN = {1099-4300},
DOI = {10.3390/e15051643}
}




@article{onnela2003dynamic,
  title={Dynamic asset trees and Black Monday},
  author={Onnela, J. P. and Chakraborti, Anirban and Kaski, Kimmo and Kertesz, Janos},
  journal={Physica A: Statistical Mechanics and its Applications},
  volume={324},
  number={1},
  pages={247--252},
  year={2003},
  publisher={Elsevier}
}


@article{onnela2003bdynamics,
  title={Dynamics of market correlations: Taxonomy and portfolio analysis},
  author={Onnela, J. P. and Chakraborti, Anirban and Kaski, Kimmo and Kertesz, Janos and Kanto, Antti},
  journal={Physical Review E},
  volume={68},
  number={5},
  pages={056110},
  year={2003},
  publisher={APS}
}


@article{wipplinger2007philippe,
  title={Philippe Jorion: Value at Risk-The New Benchmark for Managing Financial Risk},
  author={Wipplinger, Evert},
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  volume={21},
  number={3},
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  year={2007},
  publisher={Springer Science \& Business Media}
}


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	author = {Rom, Brian M. and Ferguson, Kathleen W.},
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	volume = {2},
	number = {4},
	pages = {27--33},
	year = {1993},
	doi = {10.3905/joi.2.4.27},
	publisher = {Institutional Investor Journals Umbrella},
	issn = {1068-0896},
	URL = {http://joi.iijournals.com/content/2/4/27},
	eprint = {http://joi.iijournals.com/content/2/4/27.full.pdf},
	journal = {The Journal of Investing}
}

@ARTICLE{RePEc:fau:aucocz:au2010_330,
title = {Dynamics of Stock Market Correlations},
author = {Kenett, Dror Y. and Shapira, Yoash and Madi, Asaf and Bransburg-Zabary, Sharron and Gur-Gershgoren, Gitit and Ben-Jacob, Eshel},
year = {2010},
journal = {Czech Economic Review},
volume = {4},
number = {3},
pages = {330-340},
abstract = {We present a novel approach to the study the dynamics of stock market correlations. This is achieved through an innovative visualization tool that allows an investigation of the structure and dynamics of the market, through the study of correlations. This is based on the Stock Market Holography (SMH) method recently introduced. This qualitative measure is complemented by the use of the eigenvalue entropy measure, to quantify how the information in the market changes in time. Using this innovative approach, we analyzed data from the New York Stock Exchange (NYSE), and the Tel Aviv Stock Exchange (TASE), for daily trading data for the time period of 2000–2009. This paper covers these new concepts for the study of financial markets in terms of structure and information as reflected by the changes in correlations over time.},
keywords = {Correlation; Stock Market Holography; eigenvalue entropy; sliding window},
url = {http://EconPapers.repec.org/RePEc:fau:aucocz:au2010_330}
}


@Article {PreisCurme:2014,
   author = {Curme, Chester and Preis, Tobias and Stanley, H. Eugene and Moat, Helen Susannah},
    title = {Quantifying the Semantics of Search Behavior Before Stock Market Moves},
  journal = {Proceedings of the National Academy of Sciences},
    year = {2014}
}

@article{citeulike:9128699,
    author = {Onnela, J. P. and Chakraborti, A. and Kaski, K. and Kert{\'{e}}sz, J. and Kanto, A.},
    citeulike-article-id = {9128699},
    journal = {Phys. Rev. E},
    number = {5},
    pages = {56110},
    posted-at = {2011-04-10 15:17:18},
    priority = {2},
    publisher = {APS},
    title = {{Dynamics of market correlations: Taxonomy and portfolio analysis}},
    volume = {68},
    year = {2003}
}



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  title={Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?},
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}


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}


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   author = {Lillo, Fabrizio and Miccichè, Salvatore and Tumminello, Michele and Piilo, Jyrki and Mantegna, Rosario N.},
    title = {How News Affect the Trading Behavior of Different Categories of Investors in a Financial Market},
  journal = {Working Papers},
  URL = {http://ssrn.com/abstract=2109337},
    year = {2012}
}


@incollection{Das:2010,
  title={News Analytics: Framework, Techniques and Metrics},
  author={Das, S. R.},
  year={2010},
  booktitle={The Handbook of News Analytics in Finance},
  publisher={Wiley Finance},

}

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  title={What moves stock prices?},
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  publisher={Institutional Investor Journals}
}

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     volume = {25},
     number = {2},
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     url = {http://www.jstor.org/stable/2491017},
     ISSN = {00218456},
     year = {1987},
    }

@Article{2015arXiv150706477M,
   author = {{Mizuno}, T. and {Ohnishi}, T. and {Watanabe}, T.},
    title = "{Novel and topical business news and their impact on stock market activities}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1507.06477},
     year = 2015,
    month = jul
}


@article{tetlock2007giving,
  title={Giving content to investor sentiment: The role of media in the stock market},
  author={Tetlock, P. C.},
  journal={The Journal of Finance},
  volume={62},
  number={3},
  pages={1139--1168},
  year={2007},
  publisher={Wiley Online Library}
}

@article{tetlock2008more,
  title={More than words: Quantifying language to measure firms' fundamentals},
  author={Tetlock, P. C. and Saar-Tsechansky, M. and Macskassy, S.},
  journal={The Journal of Finance},
  volume={63},
  number={3},
  pages={1437--1467},
  year={2008},
  publisher={Wiley Online Library}
}

@inbook {Mitra1:2011,
title = {Applications of news analytics in finance: A review},
author = {Mitra, L. and Mitra, G.},
publisher = {John Wiley \& Sons, Ltd.},
isbn = {9781118467411},
pages = {1--39},
booktitle = {The Handbook of News Analytics in Finance},
year = {2011},
}



@Misc{PsychSignal,
author = {PsychSignal},
 title     = "The \uppercase{P}sychSignal website",
Howpublished  =  "https://www.psychsignal.com",
year = {2018},
Note = {Last accessed on Oct 14, 2018}
}


@Misc{RAVENPACK,
author = {Ravenpack},
title = {\uppercase{R}avenpack official website.},
year = {2018},
Howpublished  = {http://www.ravenpack.com/},
Note = {Last accessed on Oct 14, 2018}
}



@article{Tobias:2013,
 author = {Alanyali, M. and Moat, H. S. and Preis, T.},
 title = {Quantifying the Relationship Between Financial News and the Stock Market},
 journal = {Sci. Rep.},
year = {2013},
 volume = {3}
}

@article{citeulike:11703961,
    author = {Luss, Ronny and d'Aspremont, Alexandre},

    doi = {10.1080/14697688.2012.672762},
    journal = {Quantitative Finance},
    month = mar,
    priority = {2},
    title = {{Predicting abnormal returns from news using text classification}},
    url = {http://www.tandfonline.com/doi/abs/10.1080/14697688.2012.672762},
    year = {2012}
}

@Article{RePEc:bla:jbfnac:v:41:y:2014:i:7-8:p:791-830,
  author={Timm O. Sprenger and Philipp G. Sandner and Andranik Tumasjan and Isabell M. Welpe},
  title={{News or Noise? Using \uppercase{t}witter to Identify and Understand Company-specific News Flow}},
  journal={Journal of Business Finance \& Accounting},
  year=2014,
  volume={41},
  number={7-8},
  pages={791-830},
  month={09},
}

@article{10.1371/journal.pone.0040014,
    author = {Bordino, Ilaria AND Battiston, Stefano AND Caldarelli, Guido AND Cristelli, Matthieu AND Ukkonen, Antti AND Weber, Ingmar},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Web Search Queries Can Predict Stock Market Volumes},
    year = {2012},
    month = {07},
    volume = {7},
    url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0040014},
    pages = {e40014},
    number = {7},
    doi = {10.1371/journal.pone.0040014}
}


@article{citeulike:13108056,
    author = {Zheludev, I. and Smith, R. and Aste, T.},
    journal = {Scientific Reports},
    month = feb,
    title = {{When Can Social Media Lead Financial Markets?}},
    volume = {4},
    year = {2014}
}

@book{mitra2011handbook,
  title={The Handbook of News Analytics in Finance},
  author={Mitra, G. and Mitra, L.},
  series={The Wiley Finance Series},
  year={2011},
  publisher={Wiley}
}


@article{RePEc:2014,
    author = {Ranco, G. AND Bordino, I. AND Bormetti, G. AND Caldarelli, G. AND Lillo, F. AND Treccani, M.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics},
    year = {2016},
    month = {01},
    volume = {11},
    url = {https://doi.org/10.1371/journal.pone.0146576},
    pages = {1-14},
    abstract = {The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.},
    number = {1},
    doi = {10.1371/journal.pone.0146576}
}


@article{Sheung:2015,
title = {News Sentiment to Market Impact and its Feedback Effect},
author = {Mo, Sheung Yin K. and Liu, Anqi and Yang, Steve Y.},
year = {2015},
month = feb,
publisher={Working Paper}
}

@article{Bollen20111,
title = "\uppercase{t}witter mood predicts the stock market ",
journal = "Journal of Computational Science ",
volume = "2",
number = "1",
pages = "1 - 8",
year = "2011",
note = "",
issn = "1877-7503",
author = "J. Bollen and H. Mao and X. Zeng",
}

@incollection{Smailovic2013,
year={2013},
isbn={978-3-642-39145-3},
booktitle={Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data},
volume={7947},
series={Lecture Notes in Computer Science},
editor={Holzinger, Andreas and Pasi, Gabriella},
doi={10.1007/978-3-642-39146-0_8},
title={Predictive Sentiment Analysis of Tweets: A Stock Market Application},
url={http://dx.doi.org/10.1007/978-3-642-39146-0_8},
publisher={Springer Berlin Heidelberg},
keywords={stock market; Twitter; predictive sentiment analysis; sentiment classification; positive sentiment probability; Granger causality},
author={Smailovic, Jasmina and Grcar, Miha and Lavrac, Nada and Znidarsic, Martin},
pages={77-88},
language={English}
}

@article{YangTwitter:2014,
author = {Steve Y. Yang and S. Y. K. Mo and X. Zhu},
title = {An Empirical Study of the Financial Community Network on \uppercase{t}witter},
year = {2014},
journal = {2014 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics}
}


@article{cerchiello2014measure,
  title={How to measure the quality of financial tweets},
  author={Cerchiello, Paola and Giudici, Paolo},
  year={2014},
  institution={University of Pavia, Department of Economics and Management}
}


@article{KIM01061996,
author = {KIM, KYUNGMO and BARNETT, GEORGE A.},
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